EUROPEAN COMMISSION DG RTD SEVENTH FRAMEWORK PROGRAMME THEME 7 TRANSPORT - SST SST.2011.RTD-1 GA No

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1 EUROPEAN COMMISSION DG RTD SEVENTH FRAMEWORK PROGRAMME THEME 7 TRANSPORT - SST SST.2011.RTD-1 GA No AsPeCSS Assessment methodologies for forward looking Integrated Pedestrian and further extension to Cyclists Safety Deliverable No. AsPeCSS D1.4 Deliverable Title Proposal for test and assessment protocol for pedestrian pre-crash systems. Dissemination level Public Written By Mervyn Edwards, Andrew Nathanson, Jolyon Carroll, TRL Marcus Wisch, Oliver Zander, BASt Nils Lubbe, Toyota Checked by Marcus Wisch (BASt) Approved by Mònica Pla (IDIADA) Issue date

2 Executive summary The overall purpose of the AsPeCSS project was to contribute towards improving the protection of vulnerable road users, in particular pedestrians and also cyclists, by developing harmonised test and assessment procedures for forward-looking integrated pedestrian safety systems. Autonomous Emergency Braking (AEB) systems for pedestrians have been predicted to offer substantial benefit. On this basis, consumer rating programmes, e.g. Euro NCAP, are developing rating schemes to encourage fitment of these systems. One of the questions that need to be answered to do this fully is how the assessment of the speed reduction offered by the AEB can be integrated with the current assessment of the passive safety for mitigation of pedestrian injury. Ideally, this should be done on a benefit related basis. The main objectives of the work reported in this deliverable were: To develop a methodology for the overall assessment of pedestrian pre-crash systems, that allows balancing of passive safety performance against active safety performance. To propose a test and assessment protocol for pedestrian pre-crash systems A benefit based methodology was developed for the overall assessment of pedestrian precrash systems, which allows balancing of passive safety performance against active safety performance. This methodology has been proposed together with AEB test protocols and the standard Euro NCAP pedestrian passive safety test protocol (version 7.1.1) as a test and assessment protocol for integrated pedestrian protection systems with pre-crash (AEB) braking. The methodology calculates the cost of pedestrian injury expected, assuming all pedestrians in the target population (i.e. pedestrians impacted by the front of a passenger car) are impacted by the car being assessed, taking into account the impact speed reduction offered by the car s AEB (if fitted) and the passive safety protection offered by the car s frontal structure. For rating purposes, this cost can be normalised by comparing it to the cost calculated for selected cars. The methodology uses the speed reductions measured in AEB tests to determine the speed at which each casualty in the target population will be impacted. The injury to each casualty is then calculated using the results from standard Euro NCAP pedestrian impactor tests and injury risk curves. This injury is converted into cost using Harm costs for the body regions tested. These costs are weighted and summed. Weighting factors were determined using accident data from Germany and GB and the results of a benefit analysis performed within this project. This resulted in German and GB versions of the methodology. Application of the methodology showed differences between the passive safety assessments of vehicles using this methodology and Euro NCAP. While the Euro NCAP ranking of good, average, and poor rated cars was reproduced with this methodology, the benefit of increasing from poor to average was larger than increasing by a similar Euro NCAP point score from average to good. This discrepancy was caused mainly by differences in the head 3/95

3 impact assessments which, in turn, were caused by the different amount of windscreen and A-pillar in the assessment area for the good, average and poor rated cars. The inclusion in the AsPeCSS methodology of discrimination in rating for severe head injury with HIC greater than 1800 and weighting of the assessment area with wrap-around-distance, which are not included in the Euro NCAP assessment, caused this difference. This leads to the question of whether or not Euro NCAP should consider inclusion of these factors, which in principle make the methodology more benefit based. This AsPeCSS assessment methodology offers that opportunity and also emphasises how important assessment of the windscreen and A- pillar areas can be when they are located in an area of high impact probability. It should be noted that assessment of the windscreen and A-pillar areas is not included in the regulatory assessment of pedestrian protection. For active safety, application of the methodology showed that the addition of an AEB system which has a performance representative of current systems, in terms of the assessment, is broadly equivalent to increasing passive safety from poor to average or average to good. Simplifications in the AsPeCSS assessment methodology, namely to not take into account the speed reductions that active safety systems may deliver when the driver brakes partially and to assume that the speed reduction for the obstructed child scenario 75% impact condition is the same as for the obstructed child scenario 50% impact condition, turned out to have no major influence on the resulting benefit estimate. Neither did the choice of head injury risk curves. Due to the availability of in-depth accident data from only two European countries, effectively, two versions of the assessment methodology, a German and a UK one have been developed. The German methodology is recommended for use in preference to the GB one as there are more data and they are believed to be more accurate. That is unless calibration specifically for GB is required, in which case the GB method may be better suited for the application. There are two possible main approaches for use of this methodology within Euro NCAP. These are: Implement the methodology in its current form within Euro NCAP for the assessment of pedestrian protection. Use the methodology to help develop weighting factors for a simpler way to combine the assessments of active and passive pedestrian protection into an overall assessment of pedestrian protection. The first approach is preferable to take into account the interactions between active and passive safety, namely the modelled shift of head impact location probabilities with car speed. This would truly be an integrated assessment. Using the second approach one could develop a rating that would be additive but not truly integrated. 4/95

4 Contents Executive summary Introduction The EU project AsPeCSS Objectives Structure of this deliverable Approach Assessment Methodology Concept Active safety testing: Exposure - impact velocity curve shift Passive safety testing: Impactor measurement and extrapolation Calculation of injury frequency Calculation of socio-economic cost Vehicle assessment: Weighting (calibration) and summing Description and development of methodology Active Safety testing: Exposure - impact velocity curve shift Exposure - impact velocity curves Mapping of accident to test scenarios: Passive safety testing: Impactor measurement and extrapolation Impactor speed extrapolation models Calculation of injury frequency Impact probability for car front Injury risk functions Calculation of socio-economic cost Vehicle assessment: Weighting (calibration) and summing Impactor test data for car representative of those in accident data Body region calibration factors Overall calibration factor Results Derivation of passive safety impactor test results for exemplar good, average and poor performing vehicles Hybrid vehicles Simplified hybrid vehicles without change in amount of windscreen area in assessment zone Assessment results Hybrid vehicles Simplified hybrid vehicles without change in amount of windscreen area in assessment zone Discussion /95

5 5.1 Assessment of passive safety Assessment of active safety Sensitivity to injury risk curves used German or GB methodology? Limitations Test and assessment protocol Autonomous Emergency Braking (AEB) test protocol Passive safety test protocol Assessment (using methodology) AEB test data: Tab SpeedInput Passive safety: Tab - Matlab_HIC Passive safety: Tab Matlab_Leg Conclusions and way forward Conclusions Way forward Acknowledgements References Annex 1: Euro NCAP draft test protocol AEB VRU systems /95

6 1 Introduction 1.1 The EU project AsPeCSS The overall purpose of the AsPeCSS project was to contribute towards improving the protection of vulnerable road users, in particular pedestrians and also cyclists, by developing harmonised test and assessment procedures for forward-looking integrated pedestrian safety systems. The main output of the project is a suite of tests and assessment methods for use in future regulatory procedures and consumer rating protocols. Implementation of such procedures and protocols should encourage widespread introduction of such systems in the vehicle fleet, resulting in a significant reduction of casualties and their injury severity for vulnerable road users. The AsPeCSS project was divided into five work packages (WPs) as follows: WP1 aimed to identify accident scenarios of pedestrians and cyclists and develop weighting factors. Furthermore, WP1 aimed to develop a methodology for the overall assessment of pedestrian pre-crash systems and to provide a benefit estimate and a proposal for test and assessment protocols for forward looking integrated pedestrian safety systems. In addition, a benefit estimate was provided for pre-crash cyclist systems and recommendations were given for the work needed to update the pedestrian test and assessment protocols to include cyclists. WP2 developed a test protocol for the assessment of the effectiveness of preventive pedestrian protection systems and a test protocol for quantifying unjustified system responses (sometimes referred to as false positives ). Additionally, prototype test targets were selected. WP3 provided specifications of new impact conditions for head and legform impactors given by the introduction of active systems. Simulations and experimental tests were conducted for vehicles with different levels of passive safety performance. Finally, WP3 generated risk curves for single vehicle points as functions of vehicle impact speed for different vehicle types. WP4 was the central work package for dissemination activities. WP5 coordinated the project in terms of research quality and on time delivery of results. 1.2 Objectives The main objectives of the work reported in this deliverable were: To develop a methodology for the overall assessment of pedestrian pre-crash systems, that allows balancing of passive safety performance against active safety performance. To propose a test and assessment protocol for pedestrian pre-crash systems 1.3 Structure of this deliverable The document is structured into this introduction chapter 1, followed by chapter 2 describing the approach followed for the work performed. Next is chapter 3 describing the 7/95

7 assessment methodology and its development. This is followed by chapter 4 detailing the results of application of this methodology to some hybrid and simple hybrid exemplar vehicles with good, average and poor passive safety protection and with and without Autonomous Emergency Braking (AEB) fitted. The results are discussed in the chapter 5. Following this is chapter 6 which describes how to use the methodology developed. Finally, there chapter 7 summarises the conclusions and way forward. 8/95

8 2 Approach While passive safety assessment is well established in regulation and consumer testing, active safety assessment has only emerged recently. An integrated safety system is one which consists of both active and passive safety devices. The current challenge is to define a methodology which integrates active and passive assessments and takes into account the influence that the active safety system has on the boundary conditions for the passive safety system. The current study focused on developing a methodology to assess integrated pedestrian systems. The main aim is to develop an assessment that is related to the benefit that the system will offer in real-world impacts in order to ensure that it is meaningful. Other objectives identified for the development of the assessment included: A fully integrated assessment is necessary to assess potentially relevant interactions of vehicle safety systems. The integrated assessment should measure benefits of safety strategies which reduce impact speeds and reduce injury risk for given impact speeds The measure of benefit needs to be clearly defined. The methodology should consider all the casualty s (AIS2+) injuries and not just the maximum AIS injury because it is the combination of all the injuries which determines the outcome for the casualty. The benefit should be expressed as a single value which is indicative of the overall benefit of the system. This would enable easy comparison of the assessments of safety systems with different strategies. Injuries sustained by all body regions and if possible from ground impacts should be considered. Both the impact area as well as impact point distribution should be aligned with actual impact probabilities to assess vehicle structures according to the risk they impose to pedestrians. Impact probabilities of particular pedestrian body regions have been shown to depend on impact speed as well as other variables, e.g. Hardy et al., 2006, Feigel, This influence should be explicitly modelled. For this, full human body model simulations could be used. The assessment methodology needs to be accurate and calibrated against real-world data, as well as simple and usable for vehicle assessment. The development of the methodology was managed within the project as follows: Overall, the methodology to assess integrated pedestrian systems was developed within WP1, specifically task 1.3. The pedestrian AEB test and assessment protocol was developed within WP2. Some functional relationships required within the methodology were developed within WP3. Examples are relationships between impactor speed and injury criteria these relationships and the need for them are explained in greater detail in the Assessment Methodology section below. 9/95

9 3 Assessment Methodology 3.1 Concept The aim of the methodology is to use the results of the pedestrian AEB tests and the standard impactor tests within Euro NCAP to give an overall assessment of a car s pedestrian active and passive safety protection on a benefit based basis. In principle, the methodology calculates the cost of injury expected assuming all casualties in the target population were impacted by the car being assessed, taking into account the impact speed reduction offered by the car s AEB (if fitted) and the passive safety protection offered by the car s frontal structure. This cost can be normalised by comparing it to the cost calculated for selected cars. The methodology consists of five main steps as described below and shown in Figure Active safety testing: Exposure - impact velocity curve shift The active safety (Autonomous emergency braking (AEB)) part of the pedestrian protection system is assessed with respect to its ability to reduce impact velocity. The test scenarios are weighted according to their contribution to injury occurrence. From each test scenario the typical speed reduction over the whole range of casualty impact speeds is derived. Using this information, the exposure impact velocity curve for the pedestrian casualty target population (i.e. pedestrians impacted by a car front) is adjusted to account for the impact speed reduction provided by the active safety system Passive safety testing: Impactor measurement and extrapolation Standard headform, upper and lower legform impactor tests (following the Euro NCAP version 6.0 or protocol) combined with manufacturer simulated data, provide impactor results for most of the car s frontal area. This provides performance data on the areas likely to be hit by a pedestrian apart from areas with a high Wrap Around Distance (WAD) such as high on the windscreen and the windscreen header rail for the headform impactor. Tests are conducted at the standard speed which approximately represents a 40 km/h pedestrian impact. Injury criteria values recorded at the standard speed are extrapolated to all other speeds experienced by the pedestrian target population Calculation of injury frequency Impact probabilities for each area of the car s front are calculated for each impactor. Using these probabilities, injury criteria measurements from step 2, injury risk curves relating these measurements to the probability of injury, and the velocity exposure data from step 1, injury risks for each AIS level are summed for tested body regions for all casualties in the target population to give injury frequency for tested body regions Calculation of socio-economic cost Injury frequencies for tested body regions are converted into costs using Harm type cost information for the injuries considered, i.e. those related to the impactor injury criteria. 10/95

10 3.1.5 Vehicle assessment: Weighting (calibration) and summing The body region costs are weighted using calibration factors and summed to give the total cost of injury assuming that all pedestrians in the target population were involved in an accident with the car being assessed. This cost is also weighted using a calibration factor to account for factors such as injuries to body regions not assessed by the impactors and injuries caused by ground impact. This cost can be compared with the cost calculated for other selected cars to give a relative assessment of the car. Calibration (weighting) factors were derived by comparing the costs calculated with those known from accident data. To do this, it was assumed that none of the cars involved in the accidents in the accident data sample had AEB fitted. Impactor test results were derived for a car with passive safety protection levels representative of those of the cars in the accident data sample. The following calibration factors were derived: 1. A factor to correct the relative cost of injury calculated for the tested body regions, i.e. head, upper leg, lower leg 2. A factor to correct the total cost of injury calculated. This should help take into account injury to body regions not tested and injury caused by ground impacts. However, it does assume that the cost of these injuries is proportional to the cost of injuries to the tested body regions. Figure 1: Main steps of methodology The assessment methodology was developed using GB and German accident data separately, so effectively two versions were developed one based on GB data and the other based on German data. 11/95

11 3.2 Description and development of methodology Active Safety testing: Exposure - impact velocity curve shift The first step of the methodology assesses the effect of the AEB part of the pedestrian protection system from its ability to reduce impact velocity. This is achieved using data from the pedestrian AEB tests to estimate the change to the baseline exposure - impact velocity curve for casualties in the target population (i.e. pedestrians impacted by a car front) if it were assumed that all these casualties were involved in an accident with the car being assessed. Clearly, if no AEB is fitted there is no change to the baseline curve Exposure - impact velocity curves Baseline exposure - impact velocity curves were developed for GB and Germany using appropriate accident data for each country (Figure 2 and Figure 3). For GB, OTS data, weighted to give the correct numbers of fatal, serious and slight casualties in the national STATS19 data, were used. For Germany, GIDAS data weighted to the German national data were used. Development of these curves is described in greater detail in Edwards et al., Casualties [thousands] Impact speed [km/h] Fatal Serious Slight Sum Figure 2: Impact speed distribution curves developed for GB. 12/95

12 3.0 Casualties [thousands] Impact speed [km/h] Fatal Serious Slight Sum Figure 3: Impact speed distribution curves developed for Germany Mapping of accident to test scenarios: Accident scenarios describe the typical situations in which pedestrians are struck in realworld accidents. Test scenarios are used to assess the performance of an AEB system in a laboratory (test track) environment and are designed to reproduce the relevant parameters of the accident scenarios that have a major effect on system performance. Mapping accident to test scenarios was needed to estimate what proportion of the casualties in the target population (i.e. in the exposure - velocity curves) would experience impact speed reductions and what magnitude of speed reductions they would experience. In another part of the project, AsPeCSS developed five test scenarios described in Table 1 (Seiniger et al., 2014). 13/95

13 Scenario Table 1: Test scenario definitions from AsPeCSS. Walking (slow) Running adult Walking adult Walking adult Walking adult child obstructed TS1 TS2 TS3 TS4 TS5 Pedestrian 3 km/h 8 km/h 5 km/h 5 km/h 5 km/h speed Dummy type Adult Adult Adult Adult Child Dummy initial Farside Farside (offside) Nearside Nearside Nearside position (offside) Vehicle speeds km/h km/h km/h km/h km/h Obscuration No No No No Yes Impact point 50 % (Centre) 50 % (Centre) 25% (Nearside) 75% (Farside (offside)) 50 % (Centre) These include scenarios to assess system performance in situations in which the view of the pedestrian is obstructed and unobstructed and for different pedestrian speeds. It is interesting to note that, assuming that the performance of an AEB system is symmetrical about the car s centreline, which testing in AsPeCSS has shown to be the case, for 50% impact point cases, changing the dummy initial position from offside to nearside or viceversa should not affect the results. In an earlier part of the project, AsPeCSS developed accident scenarios and determined what proportions of the pedestrian casualty target population were impacted in these accident scenarios (Table 2) (Wisch et al., 2013a). 14/95

14 Table 2: Overview of National Accident Data All pedestrian casualties in % (day / darkness) for GB and Germany. AccScen Categories ID Description GB (79%) Germany (63%) 1A 1B Crossing a straight road from near-side; No obstruction 26 (18/8) 19 (13/6) 2A 2B Crossing a straight road from off-side; No obstruction 13 (8/5) 18 (10/8) 3A,B 4A,B Crossing at a junction from the near- or off-side with vehicle turning or not across traffic 6 (6/0) 7 (3/4) 5A 5B Crossing a straight road from near-side; With obstruction 5 (4/1) 7 (6/1) 6A 6B Crossing a straight road from off-side; With obstruction 7 (5/2) 5 (4/1) 7A 7B Along the carriageway on a straight road; No obstruction 22 (15/7) 7 (4/3) Another important finding from the accident analysis that substantially influenced the mapping of accident scenarios to test scenarios was the lateral impact position of the pedestrian. This is because the further across the car front the pedestrian is hit, relative to the side the pedestrian is crossing from, the longer the AEB system on the car has to brake, and hence the greater the speed reduction and associated benefit. The German GIDAS data was used to determine the distribution of the lateral impact position across the front of the car by accident scenario (see Table 3) because the GB OTS data sample was not large enough to provide statistically meaningful results (Edwards et al., 2014). 15/95

15 Table 3: Proportion of cases by accident scenario and impact location, based on GIDAS years , weighted and summed for each test scenario for GB and Germany. Impact location in thirds of width of vehicle. Accident scenario Impact location on car's front for left hand drive vehicles No. of cases in accident scenario left centre right 1 19% 33% 48% % 30% 25% % 36% 28% % 35% 39% % 23% 62% % 27% 22% % 33% 50% 24 Weighted average 31% 30% 39% The proposed mapping for the accident scenarios to test scenarios both GB and Germany is shown in Table 4. 1: Crossing straight road, near-side, no obstruction Table 4: Mapping of accident to test scenarios Test scenario TS1 TS2 TS3 TS4 TS5 16.5% 16.5% 48% 19% 2: Crossing straight road, farside, no obstruction 15% 15% 45% 25% Accident scenario 3 & 4: Crossing at junction, near- or far-side, vehicle turning or not across traffic 5: Crossing straight road, near-side, with obstruction 17.75% 17.75% 38% 27% 100% 6: Crossing straight road, farside, with obstruction (DE) 15% (DE) 15% (DE) 45% (DE) 25% (GB) 100% 7: Along carriageway on straight road, no obstruction 100% 8: Not classified into scenarios 1 to 7. No speed reduction It should be noted that: Weighting to test scenarios 1 and 2 was based on a split for casualties in unobstructed centre impacts. 16/95

16 Accident scenario 6 crossing straight road from far-side with obstruction was mapped to the unobstructed test scenarios (TS1, TS2, TS3 and TS4) for the German analysis because these accidents usually involved the pedestrian crossing one carriage-way of the road before been hit by the car and therefore from the AEB system point of view, the pedestrian was not obstructed. A mapping process similar to that for unobstructed accident scenarios 1 and 2 was used with the appropriate impact location proportions in Table 3. For the GB accident data, for pedestrians classified in this category, this was not the case and they were actually obstructed, so these casualties were mapped to the obstructed scenario (TS5) as shown in Table 4. For injured pedestrians hit by the front of a car that were not categorised into accident scenarios 1 to 7 shown in Table 2, it was assumed that the AEB system would offer no speed reduction and therefore no benefit. Mapping in this manner, using proportions applied to overall exposure - velocity curve, does have a significant limitation because it intrinsically assumes that the impact velocity distributions for all accident scenarios are the same which has been shown not to be the case (Wisch et al., 2013b). However, in the future this limitation could be removed by developing exposure - velocity curves for individual accident scenarios providing the accident data sample is sufficiently large to do this. This may be the case for GIDAS, but will not be the case for OTS. For the current study it was judged that the error caused by this approximation was likely to be insignificant compared to errors caused by other approximations within the methodology. It should also be noted that for calibration of the methodology which used the results of the benefit analysis (Edwards et al., 2014), for reasons of consistency, the impact velocity reductions calculated for the benefit analysis (i.e. changes in the exposure - velocity curve) were used directly and therefore the mapping described above was not used. The impact velocity changes calculated for the benefit analysis are detailed in Edwards et al., Passive safety testing: Impactor measurement and extrapolation Standard headform, upper and lower legform impactor tests are performed following the Euro NCAP pedestrian testing protocol version 6.0 or This and the manufacturer simulated input data gives impactor results for most of the car s frontal area that is likely to be hit by a pedestrian apart from areas with a high WAD such as high on the windscreen and the windscreen header rail for the headform impactor. 17/95

17 Figure 4: Data for standard headform, upper and lower legform impactor tests performed to the Euro NCAP version 6.0 protocol. Tests are conducted at the standard speed which approximately represents a 40 km/h pedestrian impact. Injury criteria values recorded at the standard speed are extrapolated to all other speeds experienced by the pedestrian target population using the relationships described in the sub-sections below Impactor speed extrapolation models Headform Searson has developed a relationship between Head Injury Criterion (HIC) and impact speed based on the assumption that the headform impacting against the structure can be represented by a lumped mass impacting upon a linear spring (Searson et al., 2009). This relationship can be expressed as a formula that relates the HIC value to impact velocity, head mass and target location linearly. (1) = + + m is head mass, v impact velocity and L is a constant that depends on the impact position. If it is assumed that the headform mass and impact position are constant, then the following relationship can be derived assuming that a = 5/2 as derived by Searson et al., 2009 for situations where there is no bottoming out. = This relationship was confirmed by work performed within AsPeCSS, specifically Work Package 3 (WP3) (Rodarius et al., 2014). In this work many head impactor test simulations and some laboratory tests were performed with variations in impactor speed as well as other parameters, such as car type, impact position, etc. Multivariate regression was used to fit curves to the results and check the relationship derived by Searson et al., Parameter variations (beside impact speed) were limited, in order to focus on the relationship of interest, i.e. HIC to impact speed. Further, for the bonnet, the head impact results were categorized per vehicle modelled (Small Family Car (SFC), Large Family Car (LFC), Super Mini, Large Multi Person Vehicle (MPV)). For impact results on the windscreen and A-pillar no categorization was applied. All vehicle types were taken into account. Figure 5 shows the categorized head impact test and simulation results. (2) 18/95

18 Figure 5: Overview of categorized head impact test and simulation results, (left: Bonnet ) (right: windscreen and A-pillar). Only test and simulation results with the default impact angle of 50 degrees were used. No bottoming out cases were used. Further, all impact positions (IP) and head masses, adult (mass 4.5kg) and child (3.5kg), were used. Figure 6 shows the trend curve of the head impact results on a bonnet for the category SFC. In Table 5 the results of the multivariate regression are shown for the same vehicle category. The value of exponent a is zero as no variation in head mass was present in this category. Only the child head was used. The value of exponent b (2.43) lies close to the theoretical limit of 2.5 derived by Searson et al The R 2 -value shows a good correlation (>0.7). On this basis and the good agreement with other results for other vehicles and the adult headform, it was decided to use the relationship in Equation 2 above in the assessment methodology. 19/95

19 Figure 6: Trend line with ASPECSS results for category Small Family Car showing confirmation of Searson et al., 2009 HIC to impact velocity relationship. Table 5: Multivariate regression results for estimating exponents a and b. (N=34) for the category Small Family Car. c.f. Searson 2.5 The relationship between the pedestrian impact speed and the head impact speed has been investigated by a number of authors and linear correction (k) factors developed to describe differences. This factor has been shown to vary between 0.8 and 1.2 depending on various parameters (UNECE, 2003). For the current work a factor of 1.0 was assumed, mainly for reasons of simplicity Upper legform In AsPeCSS WP3, upper legform simulations with varying impact speed were performed using three different vehicle types (SFC, supermini and large MPV). Impact speed 20/95

20 relationships for Max bending moment and Sum of forces were developed using these data. Impact speed as well as impact angle were determined based on information from the detailed Human Body Modelling study of task 3.1, which is reported in Mottola et al., 2013 and Rodarius et al., It should be noted that cases, in which the impactor bottomed out at very high impact speeds (> 50 km/h), were not used for development of the relationships. The relationships developed for max bending moment and sum of forces are shown in Figure 7 and Figure 8, respectively. Figure 7: Upper leg max bending moment vs impact speed relationship. 21/95

21 Figure 8: Upper leg sum of forces vs impact speed relationship. The relationships developed varied a little depending on the type of vehicle. For simplicity, one relationship representative for all vehicles was required. The following relationships for SFC where selected on the basis that this class of vehicle should be generally more representative of all vehicles than large MPV or Supermini. Relationships developed by WP3. Max bending moment linear y= x Sum of forces linear y=0.165x In the future different relationships could be used for different classes of vehicle if this was found to be advantageous Lower legform FlexPLI In AsPeCSS WP3, lower legform tests and simulations with varying impact speed with the Flexible Pedestrian Legform Impactor (FlexPLI) were performed using three different vehicle types (SFC, supermini and large MPV). Although two lower legform impactors, namely the EEVC WG17 legform and the FlexPLI legform, are currently available for usage in regulatory tests as well as in consumer tests, it was decided to limit the work in WP3 to an investigation of the newer and more biofidelic FlexPLI impactor. Impact speed relationships for tibia bending moment, medial collateral ligament (MCL) elongation related to knee bending, anterior cruciate ligament (ACL) and posterior cruciate ligament (PCL) elongation both related to knee shear were developed using these data. It should be noted that simulations with no upper body mass were used to develop the linear relationships below: Tibia bending moment y=5.8x+72 MCL elongation y=0.4154x /95

22 PCL elongation y=0.0899x ACL elongation y=0.2441x Figure 9: FlexPLI lower leg tibia bending moment vs impact speed relationship. Figure 10: FlexPLI lower leg MCL elongation vs impact speed relationship. 23/95

23 Figure 11: FlexPLI lower leg PCL elongation vs impact speed relationship. Figure 12: FlexPLI lower leg ACL elongation vs impact speed relationship. EEVC WG13 No relationships between injury criteria and impact speed for the EEVC WG17 impactor were developed by WP3 because they focused on FlexPLI in expectation that this would be the default lower leg impactor in the near future. However, because relationships were required 24/95

24 for calibration purposes, the following ones were derived by assuming that similar relationships would be appropriate for similar injury mechanisms: Tibia deceleration: y = Dx 2.5 i.e. y 1 /y 2 = x / x on basis that it is a deceleration based injury type measure like HIC and this relationship was used for HIC. Knee shear linear y = (( )/2)x + c on basis knee shear injury mechanism related to knee shear PCL/ACL elongation for FlexPLI. Note that relationships for PCL and ACL were averaged. Knee bending linear y = x +c on basis knee bending injury mechanism related to MCL elongation for FlexPLI Sensitivity analysis was used to help check the suitability of relationships derived Calculation of injury frequency Impact probabilities for each area of the car s front are calculated for each body region represented by an impactor. Using these probabilities, injury criteria measurements, injury risk curves relating these measurements to the probability of injury, and the velocity exposure data, injury risks for each AIS level are summed for all tested body regions for all casualties in the target population to give injury frequency for tested body regions Impact probability for car front The impact probability for the car, both laterally and longitudinally, was determined. Laterally it was assumed that the impact probability was uniform for all impactors. This assumption was supported by an analysis of GIDAS data which showed that the weighted average distribution of impact location across the car was approximately even with a slight bias to the right side (nearside) - left 31%, centre 30%, right 39% (see Table 3) Other work supports this assumption (Barrow et al., 2014). Longitudinally is only relevant for the headform impactor. For this impactor, the impact probability in terms of WAD was calculated: The following speed dependent relationship between pedestrian height and the longitudinal position of head impact in terms of WAD was derived from the results of simulations with the pedestrian human body THUMS model. Details of this work are reported in Mottola et al. (2013). WAD(log(v), Pedestrian_Height)= log(v)+1.8Pedestrian_Height Note: Units: Pedestrian_Height (mm); Speed v (km/h). 25/95

25 AsPeCSS D1.4 Proposal for test and assessment protocol for pedestrian pre-crash systems Using the equation above and population height distributions measured for the UK (Department of Trade and Industry, UK 1996) longitudinal impact probabilities in terms of WAD were derived for the headform impactor. This approach effectively assumed that the UK population height distribution was representative for Europe. Figure 13: Car frontal area Injury risk functions Introduction Injury risk curves (IRC) describe the probability that a certain load will cause a specific injury (Schmitt et al., 2004). The biomechanical response of the human body needs to be established by means of post mortem human subjects (PMHS) test, volunteer tests or accident reconstruction. The human response with its injury probability needs to be transferred to those devices that are used to assess a structure by impacting it. Impact device response may or may not accurately replicate human response, thus be seen as biofidelic or not. Commonly, impact device response will differ from human response. Transfer functions are needed: These functions may be relating human response obtained in PMHS or volunteer testing and impact device response explicitly by a mathematical function. Alternatively, the relation may be implicit. In accident reconstruction human injury probability is directly related to impact device loading without consideration of the human loading (Bovenkerk et al., 2008). Statistical methods are applied to the data to construct an injury risk curve from injury risk data. These Methods include, besides others, cumulative normal distributions, the Mertz/Weber method, the Certainty Method, Logistic regression, the Consistent Threshold Estimate (CTE), and Survival Analysis (Petitjean and Trosseille, 2011). Logistic regression is a popular method for dose-response data, probably for its simplicity model set-up and interpretation. Survival Analysis, developed for failure time analysis, has recently attracted a lot of attention and has been shown to perform well in a variety of applications (Petitjean and Trosseille, 2011, Praxl, 2011 Petitjean et al., 2012, Cutcliffe et al., 2012). 26/95

26 A variety of injury criteria and injury risk curves for pedestrian impactor testing have been proposed, a review of those relevant for pedestrian and cyclist safety assessment has been conducted by Lawrence et al., 2006 and Bovenkerk et al., Injury risk curves implemented for AsPeCSS methodology Headform EEVC WG 17 (EEVC, 2002) did not develop head IRCs, but only set a limit to HIC Lawrence et al. (2006) did not discuss Headform IRCs. Pedestrian Headform IRCs based on accident reconstruction were proposed by Matsui (2004). Reconstructing accidents with a test tool and particular impact method means that the results are specific to that approach. Adoption of the results for use with another test tool or in extension of the impact conditions is subject to the assumption that these methods do not deviate from the test behaviour of the original reconstructions. This is liable to be a limitation of using historic accident reconstruction data in the context of new pedestrian impactor test procedures. However, based on the Matsui data injury probability is given for all AIS levels using a Logistic regression type called Modified Maximum Likelihood Method (MMLM) (Figure 14). This method adds a constraint to the Logistic regression, that the injury risk needs to be zero at zero stimulus (Nakahira et al., 2000) and has been subsequently criticized (Banglmaier et al., 2002; Bovenkerk et al., 2008). To assess potential issues with the MMLM method, IRCs were constructed based on the raw data of Matsui (2004) using unconstrained or standard Logistic regression of the form: β0 + β1x e P( x) = β0 + β1x 1+ e Where: P(x): Probability of injury, x: HIC, β0, β1: Coefficients of logistic regression model These Logistic regression curves are depicted together with MMLM curves in Figure 15. One can see that HIC values at 50% injury probability are about the same for either method as the curves cross. MMLM gives lower injury probability at any HIC value below the crossing point due to its constraint to give zero response at zero stimulus. As Logistic regression curves are symmetric, this leads necessarily to MMLM giving higher injury probability at any HIC value above the crossing point. Given the desire for the AsPeCCS Project to show zero injury risk with zero HIC or contact speed, the MMLM curves appear to be the more plausible ones among the two sets. However, it is acknowledged that this statement does not take into account the correlation with which the probability estimates fit the underlying data, the confidence associated with the estimate options or even whether the two estimate types are significantly different from one another. 27/95

27 Figure 14: Pedestrian Headform injury risk curves (Matsui, 2004). Figure 15: Pedestrian Headform injury risk curves using unconstrained logistic regression ( LR ) and MMLM regression ( original ). Regression coefficients for MMLM regression by Matsui (2004) were obtained in personal correspondence with the author. Coefficients for Logistic regression were computed using Matlab 2013a and are given in Table 6. 28/95

28 Table 6: Regression coefficients for head injury risk curves. Matsui (2004) MMLM Logisitic Regression β0 β1 β0 β1 AIS AIS AIS AIS Bovenkerk et al. (2008) noted that the (non-pedestrian) IRCs normally used for HIC typically have a life-threatening brain injury risk of <20% at HIC 1000, with 20% risk at about HIC 1100 and 50% risk at about 1450, so the HIC values for given injury risks that are obtained by Matsui are considerably higher. It can be extremely difficult to reconstruct an accident and get correct pedestrian kinematics if actual films of the accidents are not available: The car speed, pedestrian stance, direction etc. need to be estimated. Errors can be very large. The exact procedure is described in Japanese only, thus it is difficult to assess the quality of the reconstructions. It can be noted that Matsui (2004) verified the reconstruction of accidents by a comparison of dent depths. A similar procedure has also been used for the construction of Upper Legform injury risk by Rodmell and Lawrence (1998). Further, it should be noted that Matsui (2004) measured HIC 36 while HIC 15 is commonly used for pedestrian testing nowadays and used a 2.5 kg child Headform impactor while 3.5 kg currently used in regulatory and NCAP testing. Schmitt et al. (2004) note that the PMHS data used to establish human IRCs (Hertz, 1993) consists of short duration impacts of typically less than 12 milliseconds, the curve is applicable to both HIC 15 and HIC 36. Assuming that the pedestrian Headform reconstructions were carried out impacting hard structures, one can in turn assume short impact duration and then the data used in the development of the HIC 36 curves may also be used alongside HIC 15. Non-pedestrian IRCs have been developed by NHTSA (1995). Based on brain IRCs from Prasad and Mertz (1985) who conducted PMHS drop tests, other AIS levels were constructed in two different approaches: expanded Prasad/Mertz and lognormal curves. In a later unpublished effort, Hackney used the basic Mertz/Prasad curves derived by Langwieder to develop a series of curves representing various MAIS levels. These expanded Prasad/Mertz curves were derived by extending the relationship between the MAIS 3 and MAIS 4 curves developed from the Thoracic Trauma Index (TTI) (used to measure impact severity to the chest in side impacts) to the MAIS 4 HIC curve representing brain injury. The resulting curves (see Figure 16) represent cumulative injury probabilities given a specific HIC level (NHTSA, 1995). 29/95

29 It is difficult to assess the quality of this procedure to expand IRCs to various AIS levels, but it seems to be questionable. Figure 16: Human head injury risk expanded Prasad/Mertz (NHTSA, 1995). Lognormal curves were derived by Hertz using the following procedure (NHTSA, 1995): 1. Prasad and Mertz (1985) data was treated as censored and described as lognormal distribution 2. Skull fracture was assumed to give MAIS2+ injury 3. Data from NASS and CDS was treated as censored and used to relate car delta v to car occupant head injury 4. A relation between delta v and injury outcome was described as lognormal distribution 5. A function to relate delta v to HIC was established based on the available MAIS2+ data 6. Head injury risk curves for specific injury level or higher were described as lognormal distribution In short, the MAIS2+ curve depicted in Figure 18 is based on PMHS data, other curves were derived matching HIC to the relation between delta v and head injury observed in the field for car occupants. Probability of specific injury can be obtained by subtracting curves for specific level or higher and are depicted in Figure /95

30 100% 100% Chane of Specific Injury Level 90% 80% 70% 60% 50% 40% 30% 20% 10% MAIS1 MAIS2 MAIS3 MAIS4 MAIS5 Fatal No Injury Chance of specific injury level or higher 90% 80% 70% 60% 50% 40% 30% 20% 10% FATAL MAIS5+ MAIS4+ MAIS3+ MAIS2+ MAIS1+ No Injury 0% HIC15 0% HIC15 Figure 17: Head injury risk curves for specific injury level lognormal (NHTSA, 1995) Figure 18: Head injury risk curves for specific injury level or higher lognormal (NHTSA, 1995) The IRC have been provided as tabulated data and graph, parameter estimates for μ and σ for the lognormal distribution of the form P HIC =N log HIC μ /σ have not been noted other than for MAIS2+. For convenience, a lognormal distribution was fitted to the tabulated data using Matlab 2013a and coefficients are given in Table 7. As seen for MAIS2+ the fit on the tabulated data does not perfectly reproduce the curves developed on the raw data. However, the precision is expected to exceed the precision of using tabulated data and interpolation. Table 7: Coefficients for lognormal head injury risk curves. Fatal MAIS5+ MAIS4+ MAIS3+ MAIS2+* MAIS1+ μ σ *NHTSA(1995) gives μ= and σ= for MAIS 2+ lognormal curve These IRC are also based on reconstructions, but it may be easier to reconstruct car occupant accidents compared to pedestrian accidents. Further, cadaver test data is used as a basis while Matsui (2004) uses only reconstructions. The accuracy and applicability for pedestrian impact conditions of the relation between delta v and HIC derived from MAIS2+ car occupant head injuries is of importance for the validity of these curves. This additional step of relating head injury to HIC via delta v might introduce additional error compared to the direct relation of HIC and injury carried out by Matsui, Further, although these IRCs are developed for humans, a transfer function to the pedestrian Headform is not available or alternatively, proof of biofidelity has not been provided. 31/95

31 AsPeCSS decided to use Matsui, 2004 MMLM IRC in spite of relevant criticism. The reasons, somewhat pragmatic, for this were: The development of these IRC was based on pedestrian to car head impact data as opposed to head to car interior impact data for the NHTSA, 1995 IRC. The Matsui, 2004 MMLM and NHTSA, 1995 IRC were both tried in the model at the calibration stage (see Section 3.2.5). It was found that the Matsui, 2004 MMLM IRC gave head injury costs that were closer to the head injury costs calculated from accident data compared to the NHTSA, 1995 IRC, i.e. calibration factor closer to Upper Legform Lawrence and al. (2006) regarded EEVC WG17 IRCs for the Upper Legform as the best available data. These curves were developed by Rodmell and Lawrence, 1998 including 12 reconstructed accidents reported in Matsui et al., Logistic regression was performed as one way to obtain IRCs and a cumulative normal distribution on injury cases only was performed as an alternative. A threshold was selected as an average of the two statistical methods EEVC, Alternatives proposed by Matsui et al., 1998 and Matsui et al., 2006 were reviewed by Bovenkerk et al., 2008 as not to follow the expected trends, this indicates that there was a problem with these data. Lubbe et al., 2011 criticized the EEVC WG17 test procedure and developed IRCs for pelvis and femur fracture based on PMHS data with an explicit transfer function to a modified test set-up. A transfer function for the EEVC WG17 test set-up was not developed. AsPeCSS adopted an injury risk curve at AIS 2 level for femur and pelvis injuries as the average of the two risk curves developed by EEVC, 2002 as depicted in Figure 19. Figure 19: Injury risk curves for the Upper leg impactor (EEVC, 2002) EEVC WG17 Legform The EEVC WG 17 IRCs (EEVC, 2002) appear to have given only limited consideration on the transfer between human and EEVC WG17 Legform and justification of the shear limit (Lawrence and al., 2006; Bovenkerk et al., 2008). 32/95

32 IRCs for tibia fracture and knee ligament injury from Matsui, 2003 were identified by Lawrence et al., 2006 to represent the best current data. These curves (Figure 20) are based on accident reconstruction with the EEVC WG17 Legform and therefore naturally compensate for differences between human and impactor response. The statistical method used was MMLM logistic regression (see discussion under head injury risk curves). AsPeCSS adopted the injury risk curves from Matsui, 2003 (Figure 20) at AIS 2 level. Figure 20: Injury risk curves for the EEVC WG17 Legform impactor (Matsui, 2003) Flexible Pedestrian Legform Impactor (Flex PLI) Takahashi et al. (2012) developed IRCs for the Flexible Pedestrian Legform Impactor (Flex- PLI). In a first step, curves for human injury probability were established from PMHS testing, scaling the subjects to average size and using survival analysis with Weibull fit as the statistical method. In a second step, transfer functions from human to impactor response were developed through impact simulations. Resulting are the IRCs depicted in Figure 21 and Figure 22, which were adopted by AsPeCSS to assess the probability of tibia fracture at AIS 2 level probability of knee ligament injury at AIS 2 level. It should be noted that an alternative IRC for tibia fracture risk based on Flex PLI bending moments has been derived by BASt alongside discussions within the Informal Group on UN GTR No. 9, Phase 2 (see UNECE document number GTR9-6-08rev1) and could be used as an alternative in future analyses. 33/95

33 Figure 21: Injury risk curve for tibia fracture assessment of Flex-PLI (Takahashi et al., 2012). Figure 22: Injury risk curve MCL ligament failure assessment of Flex-PLI (Takahashi et al., 2012) Calculation of socio-economic cost Injury frequencies for tested body regions are converted into costs for tested body regions using Harm type information for the injuries considered, i.e. those related to the impactor injury criteria. The total monetary costs from Zaloshnja et al., 2003 shown in Table 8 below (based on US data) were used to convert injury frequencies for tested body regions into socio-economic costs. Table 8: Total monetary costs from Zaloshnja et al., /95

34 3.2.5 Vehicle assessment: Weighting (calibration) and summing The body region costs are weighted using calibration factors and summed to give the total cost of injury if it was assumed that all pedestrians in the target population were involved in an accident with the car being assessed. This cost is also weighted using a calibration factor to account for factors such as injuries to body regions not assessed by the impactors and injuries caused by ground impact. This cost can be compared with the cost calculated for other selected cars to give a relative assessment of the car. Calibration factors were derived to correct for the difference in the cost of injury predicted by the model and the cost of injury calculated from accident data. The following two calibration factors were derived: 1. A factor to correct the relative cost of injury calculated for the tested body regions, i.e. head, upper leg, lower leg 2. A factor to correct the total cost of injury calculated. This should help take into account injury to body regions not tested and injury caused by ground impacts. However, it does assume that the cost of these injuries is proportional to the cost of injuries to the tested body regions. Calibration factors were derived separately for each of the versions of the code, i.e. the one based on GB accident data and the one based on German data. To derive these factors, head, upper and lower leg impactor test data in the format required by the Euro NCAP pedestrian assessment protocol version 6.0 for a car representative of the cars in the accident data sample were needed. The development of these data is described below Impactor test data for car representative of those in accident data The median registration date for cars in the accident data samples was 1997 with a range of about 1987 to 2010 (Figure 23 and Figure 24). On this basis a car broadly representative of those in the accident data sample should be circa registration year Sample = 160 number of cases Year of manufacture Figure 23:Registration date for vehicles in GB (OTS) accident data sample. 35/95

35 Figure 24: Registration date for vehicles in German (GIDAS) accident data sample. It was not possible to obtain impactor test data directly for cars of this era in Euro NCAP assessment protocol version 6.0 format (i.e. impactor data for all grid points for headform impact) because it did not exist because this protocol was only introduced in It could not be generated easily either, because simulation is required to do it and simulation models of these vehicles were not available. Therefore the following approach was used to derive the necessary impactor data: Available impactor test data in a different format for a car of similar age was transformed into the format required. A Golf V tested in 2003 was chosen for this because much research work had been performed with this car which provided information to enable this transformation. Also, this vehicle was a popular typical family car well represented in the vehicle fleet. The transformed Golf V impactor data was scaled to be representative of good, average and poor rated cars registered circa These steps are described in greater detail in the sections below Step 1: Transfer of impactor results of VW Golf V from old Euro NCAP pedestrian rating to version 6.0 rating An overview of the assessed points and the corresponding results for the old Euro NCAP headform impactor tests is shown in Figure 25 below. 36/95

36 Figure 25: Euro NCAP pedestrian test results of VW Golf V (2003). A transfer of these results to the Euro NCAP assessment protocol version 6.0 was undertaken as follows: In a first step, for a visualization of test results, a five colour band for the headform area and a three colour band for the upper and lower legform area according to the latest available testing and assessment protocols (2013) were applied to the appropriate impactor readings, and the colours then allocated to the corresponding impact areas: 37/95

37 Figure 26: Application of colour bands to impact points and areas. In the next step, the colours of the assessed test areas were transferred to the adjacent areas of the same zone. Where possible, symmetry was used: Figure 27: Transformation of colour bands to adjacent and symmetrical areas. Then, the colour codes on the test areas for the worst point test results were transferred to the headform grid markup and to an upper and lower legform markup with a resolution of nine grid points each. It has to be noted that for this transformation several estimations had to be made, especially due to the significant protocol changes in terms of masses and diameters of the headform impactors as well as the redefined test areas. 38/95

38 Figure 28: Transformation of coloured test areas to grid markup for head, upper leg and lower leg. The obtained grid results were then, in a last step, transferred to the Euro NCAP spreadsheet. Figure 29: Transformation of headform grid markup to Euro NCAP spreadsheet. Finally, the colours in the Euro NCAP spreadsheet were transferred to the spreadsheet for input into the assessment methodology. In this spreadsheet, to each colour certain predefined impactor values were allocated for the headform, upper and lower legform impactor, e.g. a HIC value of 825 as the mean value of the yellow band for the headform (650 HIC < 1000) was assigned to a yellow grid point in the headform area and a knee bending angle of 17,5 as the mean value for the yellow sliding scale of the EEVC WG legform impactor (15 KBA < 20 ) was assigned to a yellow grid point in the legform area. 39/95

39 Figure 30: Transformation of impactor colour results from Euro NCAP spreadsheet to spreadsheet for input into assessment methodology and allocation of impactor values. 40/95

40 Step 2: Scaling of VW Golf V impactor data to produce data representative of cars registered circa 1997 The actual Golf V Euro NCAP 2003 impactor results were scaled to derive an estimate of a good, average and poor performing cars representative of those in the accident data. Table 9 shows the results of Euro NCAP phase 1 from /95

41 Table 9: Euro NCAP Phase 1 pedestrian test results [Source: Euro NCAP]. 42/95

42 From the Euro NCAP phase 1 pedestrian test results an estimate was made (in terms of points ratings) for good, average and poor performing vehicles of that era. Using this information and knowledge of the points ratings of more current vehicles, an estimate was made (in terms of points rating) for good, average and poor performing vehicles representative of vehicles within the accident database, i.e. vehicles registered from 1987 to Following this, factors were derived to scale the Golf V impactor results to give these points ratings. The details of this exercise are shown in Table Hybrid (Phase 1) Table 10: Scaling of Golf V pedestrian test results. Impactor Performance Good Average Poor CH 5 / 4 3 / 0 2 AH 1 / 2 1 / 4 1 UL LL CH/AH Estimate UL LL Golf V CH/AH 8.93 UL 4.46 Golf V adjustment factors LL 5.44 CH/AH UL LL Using the calculated adjustment factors, the Golf V grid results of Step I were scaled to create corresponding good, average and poor performing cars with detailed grid information for the head area and 9 point resolution for the lower extremity areas, representing cars in the database. Therefore, first a pure adjustment was applied and then an allocation of HIC values as done for the Golf V in step I was done afterwards. Figure 31 depicts the Golf V grid results that were determined in Step I, scaled to a good representative of the accident database: 43/95

43 Figure 31: Scaling of Golf V pedestrian test results to a good representative performance of car in accident database ( ). These results were then transformed into a spreadsheet for input into the assessment methodology by, allocating each colour to predefined impactor values as described in Step I: Figure 32: Transformation of scaled good performing Golf V pedestrian test results for insertion into spreadsheet for input into assessment methodology. The same procedure as described above was also applied for the scaling and transformation of average and poor results: 44/95

44 Figure 33: Scaling of Golf V pedestrian test results to an average representative performance of car in accident database ( ). Figure 34: Transformation of scaled average performing Golf V pedestrian test results for insertion into spreadsheet for input into assessment methodology. 45/95

45 Figure 35: Scaling of Golf V pedestrian test results to a poor representative performance of car in accident database ( ). Figure 36: Transformation of scaled poor performing Golf V pedestrian test results for insertion into spreadsheet for input into assessment methodology Body region calibration factors The purpose of this part of the calibration was to correct the relative cost of injury calculated for the tested body regions, i.e. head, upper leg, lower leg, to ensure it was equivalent to that seen in the accident data. Two sets of calibration factors were derived, one for each version of the model. 46/95

46 The cost of injury for the head, upper leg and lower leg body regions seen in the accident was calculated using the GIDAS (German) and OTS (GB) accident databases. For casualties in the target population, (pedestrians impacted by the front of a car) the injury to these body regions was summed (Figure 37). DE (GIDAS) Head Upper Leg Lower leg Other GB (OTS) Head Upper leg Lower leg Other MAIS 1 MAIS 2 MAIS 3 MAIS 4 MAIS 5 MAIS Figure 37: Number of injuries per body region (multiple regions may be injured per person) for casualties in target population; GB (OTS, N=160), DE (GIDAS, N=349) The costs of these injuries were calculated for each body region in a similar way as for the model described in Section using the total monetary costs from Zaloshnja et al The results of these calculations are shown in Figure 38. DE(GIDAS) GB (OTS) Head 67% Head 69% Upper Leg 17% Upper leg 12% Lower leg 17% Lower leg 19% 0% 50% 100% 0% 50% 100% Figure 38: Harm calculated from GB (OTS, N=160) and DE (GIDAS, N=349) The body region costs calculated from the accident data were compared with those predicted by the model to derive the calibration factors for the German and GB versions of the model (Table 11). Impactor test data representative of an average car in the accident data sample were used as input to the model. The derivation of these data is described in Section /95

47 Table 11: Comparison of distribution of body region costs from model and accident data and calibration factors derived to correct. Body region Distribution of body region injury cost Calibration factor Model Accident data DE Head 69.7% 66.6% Upper leg 19.7% 16.7% Lower leg 10.6% 16.7% GB Head 72.0% 68.9% Upper leg 17.9% 12.2% Lower leg 10.1% 18.9% This process was repeated using the impactor data for the good and poor performing representative cars. It was found that the calibration factors derived were not that different to those derived for the average representative car (Table 12). On the basis of this, it was decided to use the calibration factors derived for the average representative car. Table 12: Comparison of calibration factors calculated using impactor data for good, average and poor performance accident data representative cars. Good Average Poor DE (GIDAS) Head Upper leg Lower leg GB (OTS) Head Upper leg Lower leg Overall calibration factor Following application of the body region calibration factors an overall calibration factor was used to correct the total cost of injury calculated. This should help take into account the following issues: Injuries to body regions not tested and hence not assessed in the methodology. Injuries caused by ground impacts which are not assessed in the methodology. Alignment of US harm based costs used in methodology with European injury costsnote that this includes currency conversion factors. However, this implicitly assumes that the effect of these issues on the total cost is linearly related to the cost calculated by the methodology which is based on the body regions assessed only. The overall calibration factor was calculated from a comparison of the cost predicted by the model and the cost of injury estimated from the accident data. Injury cost estimated from accident data was calculated previously using both the GB and German accident data as part 48/95

48 of the benefit analysis reported in Edwards et al., In this analysis, injury costs were calculated for the following four potential situations: No AEB system fitted to cars. Current generation AEB pedestrian systems (2013+) fitted to all cars. o This system is representative of systems fitted to a few current cars. Second generation AEB pedestrian systems (2018+) fitted to all cars. o This system is representative of a future system which could be expected to be developed within the next few years. For this system test results were simulated, based on a system performance estimated using expected improvements in system component performance, such as sensor performance and brake ramp. Reference limit AEB pedestrian system (2023+) fitted to all cars. o This system is representative of a system which has the best performance currently estimated as being technically feasible. For this system, test results were simulated based on a system performance estimated using components with a maximum performance thought possible in the longer term future. Specifications of the AEB systems were developed in Edwards et al., Both the GB and German model versions were run using input data to represent the four situations described above. Impactor test data representative of an average car in the accident data sample and exposure - impact velocity curves from the benefit for each of the four situations described above were used as input data. The exposure - impact velocity curves used took into account the effect of the AEB system on the accident impact speed. The costs calculated from the benefit analysis (Edwards et al., 2014) were compared with those predicted by the model for each of the AEB systems to derive calibration factors for the GB and German model versions, Table 13 and Table 14 respectively. The calibration factors for each AEB system variant were averaged to give overall calibration factors for GB and German model versions. Table 13: Comparison of accident data and model based costs to derive calibration factors for GB model version. AEB System Cost Calibration Accident Data ( ) Model ($) factor No AEB system 1,370,605,686 4,605,055, Current generation ,251,576,208 4,165,208, nd generation ,116,081,128 3,649,391, Reference limit 986,102,173 3,181,494, Average calibration value /95

49 Table 14: Comparison of accident data and model based costs to derive calibration factors for German model version. AEB System Cost Calibration Accident Data ( ) Model ($) factor No AEB system 1,009,495,154 6,593,132, Current generation ,862,318 6,119,466, nd generation ,968,684 5,523,045, Reference limit 790,777,824 5,005,122, Average calibration value /95

50 4 Results Both versions of the assessment methodology, (German and GB) were trialled with the following input data: AEB input test data o No AEB system, i.e. zero speed reduction in five AsPeCSS test scenarios described in Table 1. o Model derived data for current generation AEB system used for calibration, i.e. predicted speed reductions for all five AsPeCSS accident scenarios described in Table 1. o Model derived data for current generation AEB system used for calibration including data for impact locations not included in AsPeCSS test scenarios and data for AEB speed reductions predicted for cases in which there was driver partial braking, i.e. predicted speed reductions for all five AsPeCSS test scenarios described in Table 1 plus the effective addition of the following test scenarios: 25% and 75% impact location obstructed test scenarios. and additional scenarios to account for AEB benefit Effective addition of test scenarios to include AEB speed reductions in cases where driver braked partially. o AsPeCSS AEB test and model data for AEB system fitted to vehicle C (see Seinger et al., 2014), i.e. measured or predicted speed reductions for vehicle C AEB system in five AsPeCCS test scenarios described in Table 1. Passive safety impactor test data o Impactor tests results representative of current good, average and poor performing vehicles. The reasons for choosing to trial the assessment methodology with these input data were: The runs with no AEB system were performed to provide a baseline assessment of the performances of the passive safety protection provided by the good, average and poor performing representative vehicles and check that these assessments aligned with the Euro NCAP assessment (rating) of their passive protection. In the benefit analysis performed previously in this project (Edwards et al., 2014) the impact speed reductions at all impact points (25, 50 & 75 %) were modelled for the obstructed scenario because it was found that the system performance varied considerably with impact location. In addition, the benefit (speed reduction) given by AEB for cases in which the driver braked partially was modelled also. For the current assessment, only the 50 % impact location point for the obstructed scenario is tested because the number of tests has to be limited for practical reasons. Therefore, for the current assessment the performance at the 75 % impact location is assumed to be the same as the 50 % impact location even though it is likely to be somewhat better. Also, it is not possible to test the performance of the AEB system in cases where the driver brakes partially, so for the current assessment it is assumed that there is no benefit (speed reduction) for these cases. The two sets of runs with the model derived data for current AEB systems were performed to investigate the effect of these assumptions. 51/95

51 The runs with AsPeCSS AEB test and model data were performed to trial the methodology with real data. Runs with the passive safety impactor test data representative of good, average and poor performing vehicles were performed to investigate the relative contributions of passive and AEB measures to the overall assessment. 4.1 Derivation of passive safety impactor test results for exemplar good, average and poor performing vehicles Hybrid vehicles To trial the AsPeCSS assessment methodology, virtual hybrid vehicles with good, average and poor pedestrian performance were created from the results of cars tested recently (circa 2013) in the Euro NCAP test programme. An overview of the method is depicted in Figure 39, in which the good performing areas (head, upper leg, lower leg) from various vehicles were combined to produce virtual hybrid vehicles with good, average and poor performance in all areas. Figure 39: Hybridization methodology. First, the Euro NCAP 2013 pedestrian database was checked for the performance of passenger cars. An overview of the Euro NCAP pedestrian test results including the corresponding degrees of fulfillment for the headform, upper and lower legform area is shown in Figure /95

52 Figure 40: Overview of Euro NCAP 2013 pedestrian assessment results. While a high portion of vehicles tested in 2013 scored maximum points in the lower legform area and scored more than 50% also in the headform area, the results in the upper legform area were far from the maximum in many cases. From these results, representatives for a good, average and poor performing car were selected on component level for the headform, upper and lower legform area, i.e. focusing on the particular impact area only (Figure 41 and Figure 42). Figure 41: Representatives for good, average and poor Euro NCAP pedestrian performance in headform area. 53/95

53 Figure 42: Representatives for good, average and poor Euro NCAP pedestrian performance in lower extremity area. The representatives on component level were then hybridized, resulting in overall good, average and poor performing hybrid vehicles (Figure 43). Figure 43: Hybridization of good, average and poor Euro NCAP pedestrian performer. 54/95

54 In a similar manner as for the Golf V (see Section ), these results were, in a final step, transformed into the spreadsheet for input into the assessment methodology. However, different to the Golf V, for the selected vehicles the information on the actual vehicle markup and impact points was not available, so that a transformation of upper and lower legform results had to be undertaken within the spreadsheet from a six areas to a nine point resolution, assuming similar performance in adjacent areas. Figure 44: Assessment methodology input spreadsheet preparation headform area (good hybrid). 55/95

55 Figure 45: Assessment methodology input spreadsheet preparation lower extremity area (good hybrid). Figure 46: Assessment methodology input spreadsheet preparation headform area (average hybrid). 56/95

56 Figure 47: Assessment methodology input spreadsheet preparation lower extremity area (average hybrid). Figure 48: Assessment methodology input spreadsheet preparation headform area (poor hybrid). 57/95

57 Figure 49: Assessment methodology input spreadsheet preparation lower extremity area (poor hybrid) Simplified hybrid vehicles without change in amount of windscreen area in assessment zone During the analysis of the results, it was found that the AsPeCSS methodology assessment for head impact protection was sensitive to changes in the amount of windscreen area in the assessment zone. To investigate how the assessment varied without changes to this, simplified hybrid vehicles were derived with no difference in the amount of windscreen area in the assessment zone. This was done by scaling the head impactor results for the average hybrid vehicle to generate good and poor Euro NCAP rated simplified hybrid vehicles. The allocated HIC values for the headform areas of the Average Hybrid vehicle were multiplied by 0.55 to produce a Good headform area with a similar Euro NCAP score as the Good Hybrid (which had a total score of 30.2, with 20.2 for the headform assessment). In the same way, the HIC values from the Average Hybrid were multiplied by 1.3 to produce a Poor headform area for use with the Poor Hybrid leg test results (from which the Poor Hybrid, described previously, had been associated with a total score of 12.2 all from the head area). Figure 50 shows the headform areas scaled simply from the Average Hybrid. 58/95

58 59/95 Figure 50: Representatives for good, average and poor Euro NCAP pedestrian performance in headform area, scaled from Average vehicle hybrid. When combined with the full and upper legform results derived for the Hybrid vehicles the newly scaled headform areas gave the new simply derived hybrids, as shown in Figure 51. Figure 51: Hybridization of good, average and poor Euro NCAP pedestrian performer, including simply scaled headform areas. N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A D R e d O ra n g e G re e n N /A N /A N /A N /A N /A N /A N /A N /A N /A G re e n O ra n g e D R e d N /A N /A N /A N /A N /A N /A D R e d O ra n g e G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n G re e n O ra n g e D R e d N /A N /A N /A N /A N /A N /A D R e d O ra n g e G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n G re e n O ra n g e D R e d N /A N /A N /A N /A N /A N /A D R e d O ra n g e G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n G re e n O ra n g e D R e d N /A N /A N /A N /A N /A N /A D R e d O ra n g e O ra n g e O ra n g e Y e lo w G re e n G re e n G re e n G re e n G re e n Y e lo w O ra n g e O ra n g e O ra n g e D R e d N /A N /A N /A N /A N /A N /A D R e d O ra n g e G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n O ra n g e D R e d N /A N /A N /A N /A N /A N /A N /A O ra n g e G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n Y e lo w N /A N /A N /A N /A N /A N /A N /A N /A Y e lo w Y e lo w G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n Y e lo w Y e lo w N /A N /A N /A N /A N /A N /A N /A N /A G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n N /A N /A N /A N /A N /A N /A N /A N /A G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n N /A N /A N /A N /A N /A N /A N /A N /A Y e lo w G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n Y e lo w N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A G re e n N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A D R e d R e d G re e n N /A N /A N /A N /A N /A N /A N /A N /A N /A G re e n R e d D R e d N /A N /A N /A N /A N /A N /A D R e d R e d G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n G re e n R e d D R e d N /A N /A N /A N /A N /A N /A D R e d R e d G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n G re e n R e d D R e d N /A N /A N /A N /A N /A N /A D R e d R e d G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n G re e n R e d D R e d N /A N /A N /A N /A N /A N /A D R e d R e d R e d R e d B ro w n O ra n g e Y e lo w Y e lo w Y e lo w Y e lo w O ra n g e R e d R e d R e d D R e d N /A N /A N /A N /A N /A N /A D R e d R e d Y e lo w O ra n g e O ra n g e O ra n g e Y e lo w Y e lo w Y e lo w Y e lo w Y e lo w G re e n Y e lo w R e d D R e d N /A N /A N /A N /A N /A N /A N /A R e d O ra n g e Y e lo w O ra n g e O ra n g e Y e lo w Y e lo w G re e n G re e n G re e n G re e n G re e n O ra n g e N /A N /A N /A N /A N /A N /A N /A N /A B ro w n B ro w n O ra n g e Y e lo w Y e lo w Y e lo w G re e n G re e n G re e n G re e n Y e lo w O ra n g e O ra n g e N /A N /A N /A N /A N /A N /A N /A N /A Y e lo w Y e lo w Y e lo w G re e n G re e n Y e lo w Y e lo w G re e n G re e n G re e n Y e lo w Y e lo w Y e lo w N /A N /A N /A N /A N /A N /A N /A N /A Y e lo w O ra n g e O ra n g e Y e lo w G re e n Y e lo w Y e lo w Y e lo w G re e n G re e n Y e lo w O ra n g e Y e lo w N /A N /A N /A N /A N /A N /A N /A N /A B ro w n O ra n g e O ra n g e Y e lo w Y e lo w Y e lo w Y e lo w Y e lo w G re e n Y e lo w O ra n g e O ra n g e B ro w n N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A O ra n g e O ra n g e Y e lo w Y e lo w Y e lo w Y e lo w Y e lo w O ra n g e O ra n g e N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A O ra n g e N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A D R e d R e d G re e n N /A N /A N /A N /A N /A N /A N /A N /A N /A G re e n R e d D R e d N /A N /A N /A N /A N /A N /A D R e d R e d G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n G re e n R e d D R e d N /A N /A N /A N /A N /A N /A D R e d R e d G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n G re e n R e d D R e d N /A N /A N /A N /A N /A N /A D R e d R e d G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n G re e n R e d D R e d N /A N /A N /A N /A N /A N /A D R e d R e d R e d R e d R e d B ro w n O ra n g e O ra n g e O ra n g e O ra n g e B ro w n R e d R e d R e d D R e d N /A N /A N /A N /A N /A N /A D R e d R e d O ra n g e B ro w n B ro w n B ro w n O ra n g e O ra n g e O ra n g e O ra n g e O ra n g e G re e n O ra n g e R e d D R e d N /A N /A N /A N /A N /A N /A N /A R e d B ro w n O ra n g e B ro w n B ro w n O ra n g e O ra n g e G re e n G re e n G re e n G re e n G re e n B ro w n N /A N /A N /A N /A N /A N /A N /A N /A R e d R e d B ro w n O ra n g e O ra n g e O ra n g e G re e n G re e n G re e n G re e n O ra n g e B ro w n B ro w n N /A N /A N /A N /A N /A N /A N /A N /A O ra n g e O ra n g e O ra n g e G re e n G re e n O ra n g e O ra n g e G re e n G re e n G re e n O ra n g e O ra n g e O ra n g e N /A N /A N /A N /A N /A N /A N /A N /A O ra n g e B ro w n O ra n g e O ra n g e G re e n O ra n g e O ra n g e O ra n g e G re e n G re e n O ra n g e B ro w n O ra n g e N /A N /A N /A N /A N /A N /A N /A N /A R e d B ro w n B ro w n O ra n g e O ra n g e O ra n g e O ra n g e O ra n g e G re e n O ra n g e B ro w n B ro w n R e d N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A B ro w n B ro w n O ra n g e O ra n g e O ra n g e O ra n g e O ra n g e B ro w n B ro w n N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A B ro w n N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A Correction factor Correction factor Correction factor Head score 20.5 Head score 15.0 Head score 12.7 Head score 20.5 Head score 15.0 Head score 12.7 Upper leg score 6 Upper leg score 2.8 Upper leg score 0 Lower leg score 6 Lower leg score 4.8 Lower leg score 0 Total score 32.5 Total score 22.6 Total score 12.7 N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A D R e d O ra n g e G re e n N /A N /A N /A N /A N /A N /A N /A N /A N /A G re e n O ra n g e D R e d N /A N /A N /A N /A N /A N /A D R e d O ra n g e G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n G re e n O ra n g e D R e d N /A N /A N /A N /A N /A N /A D R e d O ra n g e G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n G re e n O ra n g e D R e d N /A N /A N /A N /A N /A N /A D R e d O ra n g e G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n G re e n O ra n g e D R e d N /A N /A N /A N /A N /A N /A D R e d O ra n g e O ra n g e O ra n g e Y e lo w G re e n G re e n G re e n G re e n G re e n Y e lo w O ra n g e O ra n g e O ra n g e D R e d N /A N /A N /A N /A N /A N /A D R e d O ra n g e G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n O ra n g e D R e d N /A N /A N /A N /A N /A N /A N /A O ra n g e G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n Y e lo w N /A N /A N /A N /A N /A N /A N /A N /A Y e lo w Y e lo w G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n Y e lo w Y e lo w N /A N /A N /A N /A N /A N /A N /A N /A G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n N /A N /A N /A N /A N /A N /A N /A N /A G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n N /A N /A N /A N /A N /A N /A N /A N /A Y e lo w G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n Y e lo w N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A G re e n N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A D R e d R e d G re e n N /A N /A N /A N /A N /A N /A N /A N /A N /A G re e n R e d D R e d N /A N /A N /A N /A N /A N /A D R e d R e d G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n G re e n R e d D R e d N /A N /A N /A N /A N /A N /A D R e d R e d G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n G re e n R e d D R e d N /A N /A N /A N /A N /A N /A D R e d R e d G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n G re e n R e d D R e d N /A N /A N /A N /A N /A N /A D R e d R e d R e d R e d B ro w n O ra n g e Y e lo w Y e lo w Y e lo w Y e lo w O ra n g e R e d R e d R e d D R e d N /A N /A N /A N /A N /A N /A D R e d R e d Y e lo w O ra n g e O ra n g e O ra n g e Y e lo w Y e lo w Y e lo w Y e lo w Y e lo w G re e n Y e lo w R e d D R e d N /A N /A N /A N /A N /A N /A N /A R e d O ra n g e Y e lo w O ra n g e O ra n g e Y e lo w Y e lo w G re e n G re e n G re e n G re e n G re e n O ra n g e N /A N /A N /A N /A N /A N /A N /A N /A B ro w n B ro w n O ra n g e Y e lo w Y e lo w Y e lo w G re e n G re e n G re e n G re e n Y e lo w O ra n g e O ra n g e N /A N /A N /A N /A N /A N /A N /A N /A Y e lo w Y e lo w Y e lo w G re e n G re e n Y e lo w Y e lo w G re e n G re e n G re e n Y e lo w Y e lo w Y e lo w N /A N /A N /A N /A N /A N /A N /A N /A Y e lo w O ra n g e O ra n g e Y e lo w G re e n Y e lo w Y e lo w Y e lo w G re e n G re e n Y e lo w O ra n g e Y e lo w N /A N /A N /A N /A N /A N /A N /A N /A B ro w n O ra n g e O ra n g e Y e lo w Y e lo w Y e lo w Y e lo w Y e lo w G re e n Y e lo w O ra n g e O ra n g e B ro w n N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A O ra n g e O ra n g e Y e lo w Y e lo w Y e lo w Y e lo w Y e lo w O ra n g e O ra n g e N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A O ra n g e N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A D R e d R e d G re e n N /A N /A N /A N /A N /A N /A N /A N /A N /A G re e n R e d D R e d N /A N /A N /A N /A N /A N /A D R e d R e d G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n G re e n R e d D R e d N /A N /A N /A N /A N /A N /A D R e d R e d G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n G re e n R e d D R e d N /A N /A N /A N /A N /A N /A D R e d R e d G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n G re e n R e d D R e d N /A N /A N /A N /A N /A N /A D R e d R e d R e d R e d R e d B ro w n O ra n g e O ra n g e O ra n g e O ra n g e B ro w n R e d R e d R e d D R e d N /A N /A N /A N /A N /A N /A D R e d R e d O ra n g e B ro w n B ro w n B ro w n O ra n g e O ra n g e O ra n g e O ra n g e O ra n g e G re e n O ra n g e R e d D R e d N /A N /A N /A N /A N /A N /A N /A R e d B ro w n O ra n g e B ro w n B ro w n O ra n g e O ra n g e G re e n G re e n G re e n G re e n G re e n B ro w n N /A N /A N /A N /A N /A N /A N /A N /A R e d R e d B ro w n O ra n g e O ra n g e O ra n g e G re e n G re e n G re e n G re e n O ra n g e B ro w n B ro w n N /A N /A N /A N /A N /A N /A N /A N /A O ra n g e O ra n g e O ra n g e G re e n G re e n O ra n g e O ra n g e G re e n G re e n G re e n O ra n g e O ra n g e O ra n g e N /A N /A N /A N /A N /A N /A N /A N /A O ra n g e B ro w n O ra n g e O ra n g e G re e n O ra n g e O ra n g e O ra n g e G re e n G re e n O ra n g e B ro w n O ra n g e N /A N /A N /A N /A N /A N /A N /A N /A R e d B ro w n B ro w n O ra n g e O ra n g e O ra n g e O ra n g e O ra n g e G re e n O ra n g e B ro w n B ro w n R e d N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A B ro w n B ro w n O ra n g e O ra n g e O ra n g e O ra n g e O ra n g e B ro w n B ro w n N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A B ro w n N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A

59 4.2 Assessment results Hybrid vehicles The results using the German and GB versions of the assessment methodology for the hybrid vehicles are shown in Table 15 and Table 16 below, respectively. Results are shown in terms of total casualty costs assuming all cars in Germany or GB (depending on the version of the assessment used) were fitted with the system being assessed. Costs are nominally in Euros for the German version and Great Britain pounds for the GB version because the models were calibrated using the results of the benefit analyses for the respective countries (see Section ). Table 15: For German version of methodology, assessment results in terms of total casualty cost for hybrid cars with good, average and poor Euro NCAP passive safety rating fitted with various AEB systems. Percentages show costs normalised to average passive safety performance with no AEB system fitted. AEB System Passive Safety Level (Euro NCAP score rating) Good (32.2) Avg (22.6) Poor (12.2) No System 674,833,590 (97.0%) 695,655,066 (100.0%) 943,722,432 (135.7%) Current generation system model ,292,610 (83.3%) 597,640,882 (85.9%) 809,741,694 (116.4%) Current generation system model with additional impact locations and partial braking 557,356,996 (80.1%) 577,575,632 (83.0%) 780,545,282 (112.2%) AsPeCSS Vehicle C test / model results Ref: D ,954,375 (75.0%) 538,167,808 (77.4%) 725,527,777 (104.3%) 60/95

60 Table 16: For GB version of methodology, assessment results in terms of total casualty cost for hybrid cars with good, average and poor Euro NCAP passive safety rating fitted with various AEB systems. Percentages show costs normalised to average passive safety performance with no AEB system fitted. AEB System Passive Safety Level (Euro NCAP score rating) Good (32.2) Avg (22.6) Poor (12.2) No System 905,987,923 (97.0%) 934,143,898 (100.0%) 1,253,150,292 (134.1%) Current generation system model Current generation system model with additional impact locations and partial braking 780,094,731 (83.5%) 741,636,876 (79.4%) 805,629,605 (86.2%) 768,485,845 (82.3%) 1,077,328,016 (115.3%) 1,025,533,028 (109.8%) AsPeCSS Vehicle C test / model results Ref: D ,078,290 (73.6%) 708,493,419 (75.8%) 947,330,416 (101.4%) Simplified hybrid vehicles without change in amount of windscreen area in assessment zone The results using the German and GB versions of the assessment methodology for the simplified hybrid vehicles are shown in Table 17 and Table 18 below, respectively. Results are shown in terms of total casualty costs assuming all cars in Germany or GB (depending on the version of the assessment used) were fitted with the system being assessed. Table 17: For German version of methodology, assessment results in terms of total casualty cost for simplified hybrid cars with good, average and poor Euro NCAP passive safety rating fitted with various AEB systems. Percentages show costs normalised to average passive safety performance with no AEB system fitted. AEB System Good (32.5) Passive Safety Level (Euro NCAP score rating) Avg (22.6) Poor (12.7) No System 586,007,115 (84.2%) 695,655,066 (100.0%) 907,762,999 (130.5%) Current generation system model ,366,368 (73.8%) 606,915,681 (87.2%) 791,833,632 (113.8%) Current generation system model with additional impact locations and partial braking AsPeCSS Vehicle C test / model results Ref: Seiniger et al., ,593,693 (69.7%) 461,939,310 (66.4%) 571,951,002 (82.2%) 545,177,337 (78.4%) 747,640,807 (107.5%) 711,476,454 (102.3%) 61/95

61 Table 18: For GB version of methodology, assessment results in terms of total casualty cost for simplified hybrid cars with good, average and poor Euro NCAP passive safety rating fitted with various AEB systems. Percentages show costs normalised to average passive safety performance with no AEB system fitted. AEB System Passive Safety Level (Euro NCAP score rating) Good (32.5) Avg (22.6) Poor (12.7) No System 794,728,549 (85.1%) 934,143,898 (100.0%) 1,212,240,493 (129.8%) Current generation system model Current generation system model with additional impact locations and partial braking 687,290,438 (73.6%) 649,667,996 (69.5%) 805,629,605 (86.2%) 760,520,034 (81.4%) 1,044,140,230 (111.8%) 985,516,915 (105.5%) AsPeCSS Vehicle C test / model results Ref: Seiniger et al., ,618,769 (66.1%) 723,838,082 (77.5%) 937,539,240 (100.4%) 62/95

62 5 Discussion 5.1 Assessment of passive safety Examination of the assessment results for the hybrid cars for No AEB system (Table 15 and Table 16) shows that the AsPeCSS assessment of the good, average and poor performing vehicles passive safety performance aligns in terms of order with the Euro NCAP score rating but does not align in terms of scale. Specifically, the AsPeCSS assessment shows a large difference in rating between the poor and average vehicles and a small difference between average and good vehicles, whereas the Euro NCAP scores show large differences between both the poor and average vehicles and the average and good vehicles. However, examination of the assessment results for the simplified hybrid cars for No AEB system (Table 17 and Table 18) shows that the AsPeCSS assessment of the good, average and poor performing vehicles passive safety performance aligns in terms of order with the Euro NCAP score rating and better in terms of scale, i.e. there is a closer difference in the assessment (cost) between poor and average and average and good vehicles. From a breakdown of the assessment results (i.e. injury costs) for the hybrid and simple hybrid vehicles shown in Table 19 it can be seen that head injury costs are the main cause in for the different assessment results for these vehicles because they form about 80% of the total costs. 63/95

63 Table 19: Breakdown of assessment results (i.e. injury costs) in AsPeCSS assessment for hybrid and simple hybrid vehicles for German version of methodology. Body region Good (Euro NCAP score) Cost (Cost normalised to average) Hybrid Head (20.2) 547,220,039 (101%) Upper leg (6) 62,853,749 (74%) Lower leg (6) 64,759,800 (98%) Total (32.2) 674,833,590 (97%) Simple Hybrid Head (20.5) 458,393,565 (84%) Upper leg (6) 62,853,749 (74%) Lower leg (6) 64,759,801 (98%) Total (32.5) 586,007,115 (84%) Passive Safety Level Average (Euro NCAP score) Cost (Cost normalised to average) (15.0) 544,291,051 (100%) (2.8) 85,399,428 (100%) (4.8) 65,964,586 (100%) (22.6) 695,655,066 (100%) (15.0) 544,291,051 (100%) (2.8) 85,399,429 (100%) (4.8) 65,964,587 (100%) (22.6) 695,655,066 (100%) Poor (Euro NCAP score) Cost (Cost normalised to average) (12.2) 606,367,102 (111%) (0) 164,656,301 (193%) (0) 172,699,028 (262%) (12.2) 943,722,432 (136%) (12.7) 570,407,670 (105%) (0) 164,656,301 (193%) (0) 172,699,028 (262%) (12.7) 907,762,999 (131%) Further investigation found that the main reason for the differences between the head impact assessments for the poor, average and good hybrid and simple hybrid vehicles was: A different amount of windscreen and A-pillar in the assessment area which varied between the poor, average and good hybrid vehicles but was constant for the simple hybrid vehicles. This coupled with the difference between the Euro NCAP and AsPeCSS assessments for severe head injury and the effective weighting of this area in the AsPeCSS assessment compared to no weighting for the Euro NCAP assessment led to the differences in the assessments seen. The effect of the different amount of windscreen and A-pillar in the assessment area can be seen by examining Figure /95

64 N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A D R e d O ra n g e G re e n N /A N /A N /A N /A N /A N /A N /A N /A N /A G re e n O ra n g e D R e d N /A N /A N /A N /A N /A N /A D R e d O ra n g e G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n G re e n O ra n g e D R e d N /A N /A N /A N /A N /A N /A D R e d O ra n g e G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n G re e n O ra n g e D R e d N /A N /A N /A N /A N /A N /A D R e d O ra n g e G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n G re e n O ra n g e D R e d N /A N /A N /A N /A N /A N /A D R e d O ra n g e O ra n g e O ra n g e Y e lo w G re e n G re e n G re e n G re e n G re e n Y e lo w O ra n g e O ra n g e O ra n g e D R e d N /A N /A N /A N /A N /A N /A D R e d O ra n g e G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n O ra n g e D R e d N /A N /A N /A N /A N /A N /A N /A O ra n g e G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n Y e lo w N /A N /A N /A N /A N /A N /A N /A N /A Y e lo w Y e lo w G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n Y e lo w Y e lo w N /A N /A N /A N /A N /A N /A N /A N /A G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n N /A N /A N /A N /A N /A N /A N /A N /A G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n N /A N /A N /A N /A N /A N /A N /A N /A Y e lo w G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n Y e lo w N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n G re e n N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A G re e n N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A D R e d R e d G re e n N /A N /A N /A N /A N /A N /A N /A N /A N /A G re e n R e d D R e d N /A N /A N /A N /A N /A N /A D R e d R e d G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n G re e n R e d D R e d N /A N /A N /A N /A N /A N /A D R e d R e d G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n G re e n R e d D R e d N /A N /A N /A N /A N /A N /A D R e d R e d G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n G re e n R e d D R e d N /A N /A N /A N /A N /A N /A D R e d R e d R e d R e d B ro w n O ra n g e Y e lo w Y e lo w Y e lo w Y e lo w O ra n g e R e d R e d R e d D R e d N /A N /A N /A N /A N /A N /A D R e d R e d Y e lo w O ra n g e O ra n g e O ra n g e Y e lo w Y e lo w Y e lo w Y e lo w Y e lo w G re e n Y e lo w R e d D R e d N /A N /A N /A N /A N /A N /A N /A R e d O ra n g e Y e lo w O ra n g e O ra n g e Y e lo w Y e lo w G re e n G re e n G re e n G re e n G re e n O ra n g e N /A N /A N /A N /A N /A N /A N /A N /A B ro w n B ro w n O ra n g e Y e lo w Y e lo w Y e lo w G re e n G re e n G re e n G re e n Y e lo w O ra n g e O ra n g e N /A N /A N /A N /A N /A N /A N /A N /A Y e lo w Y e lo w Y e lo w G re e n G re e n Y e lo w Y e lo w G re e n G re e n G re e n Y e lo w Y e lo w Y e lo w N /A N /A N /A N /A N /A N /A N /A N /A Y e lo w O ra n g e O ra n g e Y e lo w G re e n Y e lo w Y e lo w Y e lo w G re e n G re e n Y e lo w O ra n g e Y e lo w N /A N /A N /A N /A N /A N /A N /A N /A B ro w n O ra n g e O ra n g e Y e lo w Y e lo w Y e lo w Y e lo w Y e lo w G re e n Y e lo w O ra n g e O ra n g e B ro w n N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A O ra n g e O ra n g e Y e lo w Y e lo w Y e lo w Y e lo w Y e lo w O ra n g e O ra n g e N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A O ra n g e N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A D R e d R e d G re e n N /A N /A N /A N /A N /A N /A N /A N /A N /A G re e n R e d D R e d N /A N /A N /A N /A N /A N /A D R e d R e d G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n G re e n R e d D R e d N /A N /A N /A N /A N /A N /A D R e d R e d G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n G re e n R e d D R e d N /A N /A N /A N /A N /A N /A D R e d R e d G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n D G re e n G re e n R e d D R e d N /A N /A N /A N /A N /A N /A D R e d R e d R e d R e d R e d B ro w n O ra n g e O ra n g e O ra n g e O ra n g e B ro w n R e d R e d R e d D R e d N /A N /A N /A N /A N /A N /A D R e d R e d O ra n g e B ro w n B ro w n B ro w n O ra n g e O ra n g e O ra n g e O ra n g e O ra n g e G re e n O ra n g e R e d D R e d N /A N /A N /A N /A N /A N /A N /A R e d B ro w n O ra n g e B ro w n B ro w n O ra n g e O ra n g e G re e n G re e n G re e n G re e n G re e n B ro w n N /A N /A N /A N /A N /A N /A N /A N /A R e d R e d B ro w n O ra n g e O ra n g e O ra n g e G re e n G re e n G re e n G re e n O ra n g e B ro w n B ro w n N /A N /A N /A N /A N /A N /A N /A N /A O ra n g e O ra n g e O ra n g e G re e n G re e n O ra n g e O ra n g e G re e n G re e n G re e n O ra n g e O ra n g e O ra n g e N /A N /A N /A N /A N /A N /A N /A N /A O ra n g e B ro w n O ra n g e O ra n g e G re e n O ra n g e O ra n g e O ra n g e G re e n G re e n O ra n g e B ro w n O ra n g e N /A N /A N /A N /A N /A N /A N /A N /A R e d B ro w n B ro w n O ra n g e O ra n g e O ra n g e O ra n g e O ra n g e G re e n O ra n g e B ro w n B ro w n R e d N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A B ro w n B ro w n O ra n g e O ra n g e O ra n g e O ra n g e O ra n g e B ro w n B ro w n N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A B ro w n N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A AsPeCSS D1.4 Proposal for test and assessment protocol for pedestrian pre-crash systems WAD on centreline (mm) WAD on centreline (mm) WAD on centreline (mm) WAD on centreline (mm) Correction factor Correction factor Correction factor Head score 20.5 Head score 15.0 Head score 12.7 Figure 52: Head impactor results for hybrid (top) and simplified hybrid (bottom) vehicles. It is seen that for the hybrid vehicles the default shaded green windscreen and default shaded red A-pillar areas increases between the poor and average vehicles and decreases between the average and good vehicles. This will lead to an increased difference in the AsPeCSS methodology assessment between the poor and average vehicles and a decreased difference between the average and good vehicles compared to the Euro NCAP assessment. This is demonstrated by examining the AsPeCSS assessment of the simplified hybrids in which the windscreen green and A-pillar red areas are constant and the difference between the poor, average and good vehicles for the AsPeCSS assessment is much closer to that for the Euro NCAP assessment. The effect of the difference between the Euro NCAP and AsPeCSS assessments for severe head injury is illustrated in Table 20 below. This table shows head assessment results for hypothetical average hybrid vehicles with a constant Head Injury Criterion (HIC) value over all the assessment area with values representing green (good), yellow, orange (average), brown, red (poor) and default red vehicles. It can be seen that the relative differences between colours are similar for the Euro NCAP and AsPeCSS assessments for green to red rated vehicles but are quite different between red and default red for values of high HIC where the Euro NCAP assessment is constant but the AsPeCSS assessment cost increases 65/95

65 substantially. This is caused by the injury risk curves for head injury (Figure 14) which show a substantial increase in risk for severe head injury (AIS 4+, AIS 5+) at HIC values above 1800 and the large change from red (at HIC 1,800) to default red (at HIC 10,000). The result of this is that variations in the amount of A-pillar in the assessment area changes the AsPeCSS assessment much more than the Euro NCAP one. Table 20: Comparison of Euro NCAP and AsPeCSS assessments of head injury. Head Assessment Good Average Poor Yellow Brown (Green) (Orange) (red) Default red HIC ,200 1,500 1,800 10,000* Euro NCAP Score AsPeCSS assessment injury cost 92,229, ,240, ,988, ,334, ,639,030 2,888,341,830 Percentage of average cost ,047 *A value of 10,000 was chosen on the basis that it was the upper end of that assumed realistic (~ 5,000 10,000). The differences discussed above caused by changes in the amount of windscreen and A-pillar in the assessment area, were emphasised by the difference in the weighting of this area between the Euro NCAP and AsPeCSS assessments. In the Euro NCAP assessment there is no weighting of this area. However, in the AsPeCSS assessment this area is effectively weighted because of the impact probability distribution used (Figure 53). If the AsPeCSS methodology impact probability distribution curves are considered in conjunction with the impact speed distribution curves (Figure 2 and Figure 3) it can be seen that the large proportion of fatal and serious injuries occur at speeds greater than 30 km/h and that at these speeds areas with a WAD distance of 1800 mm or greater are more likely to be impacted. The outcome of this is that a change in the protection offered in these areas influences the AsPeCSS assessment considerably more than the Euro NCAP one. Figure 53: Impact probability distribution with WAD and impact speed used in assessment methodology. 66/95

66 All of this leads to the interesting questions of whether or not factors in the AsPeCSS methodology discussed above (i.e. discrimination in rating for severe head injury with HIC greater than 1800 and weighting of the assessment area with WAD), which in principle make it more benefit based, should be included in the Euro NCAP assessment. It should be noted that the main reason these questions arose was because some of the windscreen area and the surrounding structures are included in the Euro NCAP assessment. It should be noted that none of the windscreen area is included in the regulatory assessment. 5.2 Assessment of active safety If the assessment results in Table 15 to Table 18 for the hybrid and simplified hybrid vehicles are examined it can be seen that approximately, the addition of an AEB system which has a performance representative of current systems, in terms of the assessment, is broadly equivalent to increasing passive safety from poor to average or average to good. The AsPeCSS assessment methodology does not take into account the speed reductions that active safety systems may deliver when the driver brakes partially and assumes that the speed reduction for the obstructed child scenario 75% impact condition is the same as for the obstructed child scenario 50% impact condition. These assumptions were necessary because it is not easily possible to perform tests to estimate speed reductions for when the driver brakes partially and the number of test scenarios has to be limited to limit test costs. To investigate how much this affected the results, two assessments were performed using model generated test data for a vehicle representative of having a current generation AEB system fitted. For the first assessment test data for the standard scenarios only was generated. For the second assessment additional data was generated so that the additional impact speed reduction for when the driver braked partially and the additional impact speed reduction for the obstructed child scenario 75% impact condition could be taken into account. Examination of the assessment results in Table 15 to Table 18 ( Current generation system model and Current generation system model with additional impact locations and partial braking ) shows that not taking these effects into account makes the cost reduction (i.e. benefit) about 3-5 % less. This is small compared to the difference of about 30 % between vehicles with average and poor passive safety and therefore it was decided that ignoring these effects would not cause much difference to the integrated assessment and hence was a viable way forward. 5.3 Sensitivity to injury risk curves used To investigate the sensitivity of the AsPeCSS assessment methodology to the head injury risk curves used, and assessment was performed using the NHTSA (1995) IRCs instead of the Matsui (2004) MMLM ones. This assessment was performed using the German version of the methodology only for the hybrid vehicle examples with no AEB system. It was found that this change made very little difference to the assessment results (Table 21). It should be noted that the methodology was re-calibrated with the NHTSA curves for the assessment with the NHTSA IRCS. 67/95

67 Table 21: For German version of methodology, assessment results in terms of total casualty cost for hybrid cars with good, average and poor Euro NCAP passive safety rating with no AEB system fitted, using Matsui (2004) MMLM and NHTSA (1995) head injury risk curves. Percentages show costs normalised to average passive safety performance using the Matsui (2004) MMLM head injury risk curves. Head injury risk curves used Passive Safety Level (Euro NCAP score rating) Avg (22.6) Good (32.2) Poor (12.2) Matsui (2004) MMLM 674,833,590 (97.0%) 695,655,066 (100.0%) 943,722,432 (135.7%) NHTSA (1995) 666,239,215 (95.8%) 692,064,506 (99.5%) 950,840,673 (136.7%) 5.4 German or GB methodology? There are some differences between the German and GB assessment methodologies. These are: The German methodology uses a casualty impact speed distribution derived from the German GIDAS accident data whereas the GB methodology uses one derived from the GB OTS accident data. There are quite large differences between these distributions (see Figure 2 and Figure 3) There is a difference in the mapping of the accident scenarios to the test scenarios, namely in percentages and which test scenario the offside obstructed scenario is mapped to; for German mapped to unobstructed (TS2), for GB mapped to obstructed (TS5). The German methodology is calibrated using GIDAS accident data and results of the benefit analysis for Germany whereas the GB methodology is calibrated using the OTS accident data and results of the benefit analysis for GB. The cause of this is likely to be related to a slight bias in the OTS accident sampling to more serious accidents (Edwards et al., 2014) In conclusion, there is not a large difference between the German and GB methodologies. However, there is some difference between the casualty impact speed distributions, in particular there are far more casualties (mainly slight) at the lower speeds for the German distribution compared to the GB distribution. It is likely that a large contributory factor to this difference is the bias of the OTS accident sampling to more serious accidents. This is caused by factors such as: Low speed accidents often result in only slight injuries and hence are less likely to be reported to the police quickly which results in the accident data collection team not knowing about them and hence not attending and collecting relevant data. Also these types of accidents tend to be cleared up more quickly and may be cleared up before the accident data team arrives at the scene. Therefore, on this basis, the German methodology is recommended for use in preference to the GB one, unless calibration specifically for GB is required. 68/95

68 5.5 Limitations The major limitation within the methodology is the assumption used implicitly during calibration because a simple multiplication factor is used. This is that the cost of casualty injuries to body areas, such as the thorax, not assessed by the impactors (headform, upper and lower legform), and other casualty injuries such as those caused by ground impact, are related linearly to casualty injuries assessed by the impactors, i.e. head, upper leg / pelvic and lower leg injuries. However, examination of the calibration results indicates that this assumption may be valid. Specifically, the calibration factors developed using the different AEB systems are very similar with a maximum variation of less than 2 % from the average for GB and Germany. This is an indication that relationships are approximately linear because if they were not the calibration factor would likely change more. Other limitations include: Accuracy of impactor criteria to speed scaling relationships and disregarding of bottoming out. Validity and accuracy of injury risk curves. Validity and accuracy of WAD relationship with speed and pedestrian height for head impact. No account of effect of vehicle pitching when braking. How well the test scenarios represent the accident scenarios that are mapped to them. For example, at present because only basic test scenarios have been developed, accident scenarios such as crossing a straight road from near-side, no obstruction which occur in daylight and night street conditions is mapped as a whole to a test scenario which is conducted in daylight conditions. If the performance of the AEB system is dependent on the lighting conditions the current methodology will not show these differences. 69/95

69 6 Test and assessment protocol 6.1 Autonomous Emergency Braking (AEB) test protocol The definition of a test protocol for pedestrian AEB was part of Work Package 2 of the project. However, during the progress of the project there was collaboration between AsPeCSS and Euro NCAP on the development of the AEB test protocol. Indeed, the only difference is in the test scenarios. AsPeCSS has one test scenario in addition to the Euro NCAP ones, namely the walking slow unobscured scenario, TS1 in Table 1. For this reason, for the test protocol, the reader is referred to the Euro NCAP test protocol with the note that AsPeCSS requires an additional test scenario to be run, namely the walking slow unobscured scenario, TS1 in Table 1. The current draft of the Euro NCAP AEB pedestrian test protocol is shown in Annex Passive safety test protocol Headform, upper and lower legform tests should be performed to the Euro NCAP test protocol version (Euro NCAP (2013a)). At the beginning of this document there is the following list of information required from the vehicle manufacturer: 1. Manufacturer grid marking coordinates relative to an identifiable location on the vehicle. 2. Predicted colour or HIC data clearly identifying defaulted points. 3. Justification for all blue points. 4. Number of Manufacturer funded verification headform tests max (10). 5. Number of Manufacturer funded blue point tests (8 max). 6. Manufacturer funded upper legform to bonnet leading edge tests and legform to bumper tests. 7. Active hood description and supporting data (where applicable). 8. Details of the vehicle s normal ride attitude, e.g. wheel arch height. For point two, HIC data is required, not just colour data. 6.3 Assessment (using methodology) The AsPeCSS methodology has been implemented in a Matlab software programme. Input data is entered into the programme via the appropriate tabs in an excel spreadsheet as described in the sections below.

70 6.3.1 AEB test data: Tab SpeedInput Please note that test data for all AsPeCSS test scenarios is required and final speeds should be entered, not speed reductions (Figure 54). Figure 54: Excel spreadsheet tab for input of AEB test results. 71/95

71 6.3.2 Passive safety: Tab - Matlab_HIC HIC values for the grid defined in Euro NCAP, determined from simulation and as used for the Euro NCAP assessment version 7.0 (Euro NCAP, should be entered into this tab. The correction factor defined in the Euro NCAP assessment protocol version 7.0 (Euro NCAP (2013b)) should be applied if necessary. Figure 55: Excel spreadsheet tab for input of passive safety headform simulation / test results. 72/95

72 6.3.3 Passive safety: Tab Matlab_Leg Upper and lower leg impactor test data should be entered into this tab (Figure 56). Provision has been made to use either the Flex-PLI or EEVC WG17 lower legform impactors. However, it should be noted that the Euro NCAP version 7.0 protocol specifies the FlexPLI. Figure 56: Excel spreadsheet tab for input of passive safety upper and lower test results. 73/95

73 7 Conclusions and way forward 7.1 Conclusions A benefit based methodology was developed for the overall assessment of pedestrian precrash systems, which allows balancing of passive safety performance against active safety performance. This methodology has been proposed together with AEB test protocols and the standard Euro NCAP pedestrian passive safety test protocol (version 7.1.1) as a test and assessment protocol for integrated pedestrian protection systems with pre-crash (AEB) braking. Application of the methodology showed differences between the passive safety assessments of vehicles using this methodology and Euro NCAP. While the Euro NCAP ranking of good, average, and poor rated cars was reproduced with this methodology, the benefit of increasing by a similar Euro NCAP point score from poor to average was larger than increasing from average to good. This was caused mainly by differences in the head impact assessments which, in turn, were caused by the different amount of windscreen and A-pillar in the assessment area for the good, average and poor rated cars. The inclusion in the AsPeCSS methodology of discrimination in rating for severe head injury with HIC greater than 1800 and weighting of the assessment area with WAD, which are not included in the Euro NCAP assessment, caused this difference. This leads to the question of whether or not Euro NCAP should consider inclusion of these factors, which in principle make the methodology more benefit based. This AsPeCSS assessment methodology offers that opportunity and also emphasizes how important assessment of the windscreen and A-pillar areas can be when they are located in an area of high impact probability. It should be noted that assessment of the windscreen and A-pillar areas is not included in the regulatory assessment of pedestrian protection. For active safety, application of the methodology showed that the addition of an AEB system which has a performance representative of current systems, in terms of the assessment, is broadly equivalent to increasing passive safety from poor to average or average to good. Simplifications in the AsPeCSS assessment methodology, namely to not take into account the speed reductions that active safety systems may deliver when the driver brakes partially and to assume that the speed reduction for the obstructed child scenario 75% impact condition is the same as for the obstructed child scenario 50% impact condition, did not have a major influence on the resulting benefit estimate. Neither did the choice of head IRCs. While effectively two versions of the assessment methodology, a German and a GB one have been fully developed, the German methodology is recommended for use in preference to the GB one for reasons of more accurate data, unless calibration specifically for GB is required. 7.2 Way forward There are two possible main approaches for use of this methodology within Euro NCAP. These are: Implement the methodology in its current form within Euro NCAP for the assessment of pedestrian protection.

74 Use the methodology to help develop weighting factors for a simpler way to combine the assessments of active and passive pedestrian protection into an overall assessment of pedestrian protection. The first approach is preferable to take into account the interactions between active and passive safety, namely the modelled shift of head impact location probabilities with car speed. This would truly be an integrated assessment. Using the second approach one could develop a rating that would be additive but not truly integrated. 75/95

75 8 Acknowledgements The authors gratefully acknowledge the support of the European Commission and the German Federal Ministry of Transport, Building and Urban Development and thank other ASPECSS project partners for their input into this work. They also acknowledge the UK Department for Transport for permitting the use of the OTS (On-The-Spot) accident study. The OTS data forms part of the Road Accident In Depth Studies database, further information can be found at 76/95

76 9 References Banglmaier R, Wang, L, Prasad, P (2002). Various statistical methods for the analysis of experimental chest compression data, American Statistical Association, Spring Research Conference - Section on Physical & Engineering Sciences. Barrow A, Reeves C, Carroll J, Cuerden R, Liers H, Marschner M and Broertjes P (2014). Analysis of pedestrian accident leg contacts and distribution of contact points across the vehicle front. 6th International Expert Symposium on Accident Research (ESAR), Hannover, Germany, June Bovenkerk, J., Hardy, R.N., Neal-Sturgess, C.E., Hardy, B.J., van Schijndel - de Nooij, M., Willinger, R., Guerra, L.J., and Martinez, L. (2008), Biomechanics of real world injuries and their associated injury criteria, APROSYS, report number AP-SP33-001R. Cutcliffe HC, Schmidt AL, Lucas JE, Bass CR (2012). How Few? Bayesian Statistics in Injury Biomechanics, Stapp Car Crash Journal, Vol. 56, pp Department of Trade and Industry (DTI), UK (1996). The handbook of Adult Anthropometric and Strength measurements Data for design safety, Institute for occupational ergonomics, University of Nottingham. Edwards M, Nathanson A, Wisch M (2014). Benefit estimate and assessment methodologies for pre-crash braking part of forward-looking integrated pedestrian safety systems, AsPeCSS Deliverable EEVC WG17 (2002). Improved test methods to evaluate pedestrian protection afforded by passenger cars, EEVC Working Group 17 Report. Mottola E, Rodarius C, Schaub S (2013). Pedestrian kinematics and specifications of new impact conditions for head- and leg-form impactors, AsPeCSS Deliverable Euro NCAP (2013a) Euro NCAP Pedestrian testing protocol, version 7.1.1, December fb-86ae-c43f19641b2f.pdf Euro NCAP (2013b) Euro NCAP Pedestrian protection assessment protocol, version 7.0, February Feigel H (2012), Integriertes Bremssystem ohne funktionale Kompromisse [Integrated brake system without functional compromises]., Automobiltechnische Zeitschrift. 2012; 7-8: , /95

77 Hardy B, Lawrence G, Knight I and Carroll J. (2006) A study on the feasibility of measures relating to the protection of pedestrians and other vulnerable road users, TRL, Wokingham, Hertz, E (1993) A Note on the Head Injury Criterion (HIC) as a Predictor of the Risk of Skull Fracture. In: 37th Annual Proceedings, Association for the Advanceent of Automotive Medicine (AAAM), pp Lawrence G J L, Hardy B J, Carroll J A, Donaldson W M S, Visvikis C and Peel D A (2006). A study on the feasibility of measures relating to the protection of pedestrians and othervulnerable road users Final Updated by Hardy B J, Lawrence G J L, Knight I M and Carroll J A. TRL Limited unpublished Project Report UPR/VE/045/06 under Contract No. ENTR/05/17.01 to the European Commission. Lubbe N, Hikichi H, Takahashi H, Davidsson J (2011). Reviewof the Euro NCAP upper leg test. In 22nd Enhanced Safety of Vehicles Conference, Matsui Y, Ishikawa H and Sasaki A (1998). Validation of pedestrian upper legform impact tests - Reconstruction of pedestrian accidents, Paper 98-S10-O-05, In 16th Enhanced Safety of Vehicles Conference, Matsui. Y. (2003). New injury reference values determined for TRL legform impactor from accident reconstruction test, International Journal of Crashworthiness 8 (2) Matsui Y (2004). Proposal of injury risk curves for evaluating pedestrian head injury risk using headform impactor based on accident reconstruction. Japanese (English abstract, tables and figures). JSAE paper , JSAE Transactions, 35 (4) Matsui Y, Ishikawa H and Sasaki A (2006). Proposal of injury risk curves for evaluating pedestrian femur/pelvis injury risk using EEVC upper legform impactor based on accident reconstruction. International Journal of Crashworthiness 11 (2) Y. Nakahira, K. Furukawa, H. Niimi, T. Ishihara, K. Miki, Matsuoka F (2000). A Combined Evaluation Method and A Modified Maximum Likelihood Method for Injury Risk Curves, Proceeding sof IRCOBI Conference NHTSA (1995). Final Economic Assessment, FMVSS No. 201, Upper Interior Head Protection, Docket No. NHTSA item ID-0003, August 8, Available at Petitjean A, Trosseille X (2011). Statistical Simulations to Evaluate the Methods of the Construction of Injury Risk Curves, Stapp Car Crash Journal, Vol. 55 ( Petitjean A, Trosseille X, Praxl N, Hynd D, Irwin A (2012). Injury Risk Curves for the WorldSID 50th Male Dummy, Stapp Car Crash Journal, Vol /95

78 Praxl, N (2011). How Reliable are Injury Risk Curves? In 22nd Enhanced Safety of Vehicles Conference, Prasad P, Mertz HJ (1985). The Position of the U.S. Delegation to the ISO Working Group 6 on the Use of HIC in the Automotive Environment. In SAE, paper no Rodarius C, Elrofai H, Meijer R, Nuss F (2014). Data analysis for construction of injury risk curves, AsPeCSS Deliverable Rodmell C and Lawrence G J L (1998a), Comparison between dose-response and cumulative methods of injury risk analysis and implications on the JARI injury risk analysis. TRL committee paper for EEVC WG17, November 1998, EEVC WG17 / Doc 111. Wokingham, UK: TRL Limited. Searson D, Anderson R, Ponte G, van den Berg A (2009). Hedaform impact test performance of vehicles under the GTR on pedestrian safety, CASR report series, CAS Schmitt KU, Niederer P., Walz F. (2004), Trauma Biomechanics Introduction to Accidental Injury Springer-Verlag, Berlin Heidelberg New York. Seiniger P, Bartels O, Kunert M, Schaller T. (2014). Preventive Pedestrian Protection Test Procedures and Results, AsPeCSS Deliverable Takahashi Y, Matsuoka F, Okuyama H, Imaizumi I(2012). Development of Injury Probability Functions for the Flexible Pedestrian Legform Impactor, SAE Int. J. Passeng. Cars - Mech. Syst. 5(1):2012, doi: / UNECE (2003). GRSP informal group on pedestrian safety - 6th meeting - IHRA Computer Simulation Results - After Discussion -, GR/PS/61. Accessed on-line July 2014: Wisch M, Seiniger P, Pastor C, Edwards M, Visvikis C and Reeves C (2013a). Scenarios and weighting factors for pre-crash assessment of integrated pedestrian safety systems, EC FP7 AsPeCSS project Deliverable Wisch M, Seiniger P, Edwards M, Schaller T, Pla M, Aparicio A, Geronimi S and Lubbe N, (2013b). European project AsPeCSS --- Interim result: Development of test scenarios based on identified accident scenarios, in 23rd Enhanced Safety of Vehicles Conference, Zaloshnja E, Miller T, Romano E, Spicer R (2003). Crash costs by body part injured, fracture involvement, and threat to life severity, United States, 2000, Accident Analysis and Prevention, 36 (2004) pp /95

79 10 Annex 1: Euro NCAP draft test protocol AEB VRU systems 80/95

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