PEGASUS Method for Assessment of Highly Automated Driving Function

Transcription

1 PEGASUS Method for of Highly Automated Driving Function

2 Introduction to the Research Project Project for the establishment of generally accepted quality criteria, tools and methods as well as scenarios and situations for the release of HAD functions. 42 Months Term 17 German Partners January 1 st, 2016 June 30 th, 2019 OEM: Audi, BMW, Daimler, Opel, Volkswagen Tier 1: Bosch, Continental Test Lab: TÜV SÜD SMB: fka, imar, IPG, QTronic, TraceTronic, VIRES Scientific institutes: DLR, TU Darmstadt 12 Subcontracts Project Volume i.a. IFR, ika, OFFIS approx. 34,5 Mio. EUR Subsidies: 16,3 Mio. EUR Personnel Deployment approx man-month or 149 man-years 2

3 Introduction to the Research Project Resulting Starting Position Automated Driving Together with electric driving, automated driving is tomorrow s subject matter. Basic functionality is technologically given Has been demonstrated in various projects High standards regarding quality and performance of the automated vehicle Measures that product needs to meet Existing measures for testing and release are insufficient, too costintensive and too complex Consequently, the introduction of highly automated driving features today can only be achieved with great expenditure. 3

4 Introduction to the Research Project Major Questions of the Project What level of performance is expected of an automated vehicle? How can we verify that it achieves the desired performance consistently? Scenario Analysis & Quality Measures Implementation Process Testing Reflection of Results & Embedding What human capacity does the application require? What about technical capacity? Is it sufficiently accepted? Which criteria and measures can be deducted from it? Which tools, methods and processes are necessary? How can completeness of relevant test runs be ensured? What do the criteria and measures for these test runs look like? What can be tested in labs or in simulation? What must be tested on proving grounds, what must be tested on the road? Is the concept sustainable? How does the process of embedding work? 4

5 Zentrale Fragestellungen in PEGASUS PEGASUS approach to answer the question How safe is safe enough and how can we verify that it achieves the desired performance consistently? is: Method for of Highly Automated Driving Function 5

6 V1.4 Status Contribution to Safety PEGASUS Method for of Highly Automated Driving Function (HAD-F) Evidence of Highly Automated Driving Function Release Test Evaluation Test Execution Test Case Derivation Processing Contribution to Safety Statement Risk Test Results Evaluation and Classification Test Data Test HAD-F: Proving Ground Real World Drive Test Cases Application of Test Concept incl. Variation Method 12 Space of Logical Test Cases Use Case, Knowledge, Data central decentral Data / Content Procedure Workflow Process Instruction 1 Knowledge: Laws, Standards, Guidelines,... 2 Daten: Test Drive Simulator FOT/NDS Accident Data processing Requirementsdefintion 3 Requirements Analysis Systematic Identification of 5 4 Preprocessing / Reconstruction 6 Process Guidelines + Metrics for HAD Data in PEGASUS- Format Integration Pass Criteria Logical + Parameter Space Preparation for Test Concept Application of Metrics + Mapping to Logical Source of Information Evaluation & Conversion

7 V1.4 Status PEGASUS Method for of Highly Automated Driving Function (HAD-F) Evidence 21 Contribution to Safety Goal: Safety argument Contribution to Safety Statement Risk of Highly Automated Driving Function Release Test Evaluation Test Execution Test Case Derivation Test Results Evaluation and Classification Test Data Test HAD-F: Proving Ground Real World Drive Test Cases Application of Test Concept incl. Variation Method Processing 12 Space of Logical Test Cases Use Case, Knowledge, Data central decentral Start: Data / Content Use-Case Procedure Workflow Process Instruction 1 Knowledge: Laws, Standards, Guidelines,... 2 Daten: Test Drive Simulator FOT/NDS Accident Data processing Requirementsdefintion 3 Requirements Analysis Systematic Identification of 5 4 Preprocessing / Reconstruction 6 Process Guidelines + Metrics for HAD Data in PEGASUS- Format Integration Pass Criteria Logical + Parameter Space Preparation for Test Concept Application of Metrics + Mapping to Logical Source of Information Evaluation & Conversion

8 V1.4 Status PEGASUS Method for of Highly Automated Driving Function (HAD-F) Evidence of Highly Automated Driving Function Goal: Safety argument Requirementsdefintion Start: Use-Case Data processing 8

9 V1.4 Status PEGASUS Method for of Highly Automated Driving Function (HAD-F) Evidence of Highly Automated Driving Function Requirementsdefintion Use Case, Knowledge, Data Data processing 9

10 Use Case Safeguarding of Level 3 (Highly Automated Driving) function Based on an application-oriented example, highway chauffeur Basic function: Highways or highway-like roads incl. road markings Speed km/h Automated following in stop & go traffic jams Automated lane changing Automated emergency braking and collision avoidance Construction sites Automated exiting off the highway Extreme weather conditions source: VW 10

11 V1.4 Status PEGASUS Method for of Highly Automated Driving Function (HAD-F) Evidence of Highly Automated Driving Function Requirementsdefintion Use Case, Knowledge, Data 1 Knowledge: Laws, Standards, Guidelines,... 3 Requirements Analysis 6 Process Guidelines + Metrics for HAD 2 central decentral Data / Content Procedure Source of Information Evaluation & Conversion Workflow Process Instruction Data processing 11

12 Social acceptance How good is good enough? Which functional performance does a highly automated druving function need to reach a social acceptance? % Ø? Autopilot? Human driver less good good realy good Driving skills 12

13 V1.4 Status PEGASUS Method for of Highly Automated Driving Function (HAD-F) Evidence of Highly Automated Driving Function Requirementsdefintion Use Case, Knowledge, Data 1 Knowledge: Laws, Standards, Guidelines,... 3 Requirements Analysis 6 Process Guidelines + Metrics for HAD 2 Daten: central decentral Data / Content Procedure Test Drive Simulator FOT/NDS Accident Source of Information Workflow Process Instruction Data processing 13

14 Input data NDS / FOT Simulator Real world crash source: UDRIVE, IPG, Audi, DLR 14

15 V1.4 Status PEGASUS Method for of Highly Automated Driving Function (HAD-F) Evidence of Highly Automated Driving Function Requirementsdefintion Use Case, Knowledge, Data central decentral Data / Content Procedure Workflow Process Instruction 1 Knowledge: Laws, Standards, Guidelines,... 2 Daten: Test Drive Simulator FOT/NDS Accident Data processing 3 Requirements Analysis Systematic Identification of 5 4 Preprocessing / Reconstruction 6 Process Guidelines + Metrics for HAD Data in PEGASUS- Format Logical + Parameter Space Application of Metrics + Mapping to Logical Source of Information Evaluation & Conversion

16 and possibilities for description Layer model Level of abstraction Functional scenarios Logical scernarios Concrete scenarios 5 4 Base road network: Three-lane motorway in a curve, 100 km/h speed limit indicated by traffic signs Stationäre Objekte: - Basisstrecke: Lane width Curve radius Position traffic sign Stationäre Objekte: - [2..4] m [0,6..0,9] km [0..200] m Basisstrecke: Lane width 3 Curve radius 0,7 km Position traffic sign 150 m Stationäre Objekte: - Bewegliche Objekte: Bewegliche Objekte: Bewegliche Objekte: 3 Ego vehicle, Traffic jam; Interaction: Ego in maneuver approaching on the middle lane, traffic jam moves slowly End of traffic jam [ ] m Traffic jam speed [0..30] km/h Ego distance [ ] m Ego speed [ ] km/h Stauende_Pos Stau_Geschw. Ego_Abstand Ego_Geschw. 40 m 30 km/h 200 m 100 km/h 2 Umwelt: Summer, rain Umwelt: Temperature [10..40] C Droplet size [ ] µm rainfall [0,1..10] mm/h Umwelt: Temperature 20 C Droplet size 30 µm rainfall 2 mm/h 1 Number of scenarios Level of abstraction sourcee: fka, TU BS 16

17 V1.4 Status PEGASUS Method for of Highly Automated Driving Function (HAD-F) Evidence of Highly Automated Driving Function Processing 12 Space of Logical Test Cases Use Case, Knowledge, Data central decentral Data / Content Procedure Workflow Process Instruction 1 Knowledge: Laws, Standards, Guidelines,... 2 Daten: Test Drive Simulator FOT/NDS Accident Data processing Requirementsdefintion 3 Requirements Analysis Systematic Identification of 5 4 Preprocessing / Reconstruction 6 Process Guidelines + Metrics for HAD Data in PEGASUS- Format Integration Pass Criteria Logical + Parameter Space Preparation for Test Concept Application of Metrics + Mapping to Logical Source of Information Evaluation & Conversion

18 V1.4 Status PEGASUS Method for of Highly Automated Driving Function (HAD-F) Evidence of Highly Automated Driving Function Test Case Derivation Processing 14 Test Cases 13 Application of Test Concept incl. Variation Method 12 Space of Logical Test Cases Use Case, Knowledge, Data central decentral Data / Content Procedure Workflow Process Instruction 1 Knowledge: Laws, Standards, Guidelines,... 2 Daten: Test Drive Simulator FOT/NDS Accident Data processing Requirementsdefintion 3 Requirements Analysis Systematic Identification of 5 4 Preprocessing / Reconstruction 6 Process Guidelines + Metrics for HAD Data in PEGASUS- Format Integration Pass Criteria Logical + Parameter Space Preparation for Test Concept Application of Metrics + Mapping to Logical Source of Information Evaluation & Conversion

19 Test concept Replay 2Sim Test object: Functional implementation of highway chauffeur Test level: Functional Test Test platform : Pass-/Fail- Criteria Space of logical test cases all logical scenarios Allocation of test cases to test platform selected scenarios Test platform Test platform Test ground Automatized variation of parameter Manual selection of scenarios/ parameter (e.g. tests as per ECE R79, rating tests) Specific scenarios Feedback Concrete scenarios Critical cases Test execution looking for critical cases (e.g. accidents) Comparison Test execution Driving on TG + validation simulation Evaluated concrete scenarios (Pass/Fail) Surprises Logical scenario + parameter space + exposure of parameter no direct link to space of logical test cases Test platform Field Test execution of real world drive with guidelines: Route Weather Time Specific features Test object: Overall system highway chaffeur including vehicle behavior Test level: Vehicle test Test platform : Test ground/ field Measured data for database Measured data for Replay 2Sim 19

20 V1.4 Status PEGASUS Method for of Highly Automated Driving Function (HAD-F) Evidence of Highly Automated Driving Function Test Evaluation Test Execution Test Case Derivation Processing Test Results Evaluation and Classification Test Data Test HAD-F: Proving Ground Real World Drive Test Cases Application of Test Concept incl. Variation Method 12 Space of Logical Test Cases Use Case, Knowledge, Data central decentral Data / Content Procedure Workflow Process Instruction 1 Knowledge: Laws, Standards, Guidelines,... 2 Daten: Test Drive Simulator FOT/NDS Accident Data processing Requirementsdefintion 3 Requirements Analysis Systematic Identification of 5 4 Preprocessing / Reconstruction 6 Process Guidelines + Metrics for HAD Data in PEGASUS- Format Integration Pass Criteria Logical + Parameter Space Preparation for Test Concept Application of Metrics + Mapping to Logical Source of Information Evaluation & Conversion

21 Example: Software in the Loop 21

22 V1.4 Status Contribution to Safety PEGASUS Method for of Highly Automated Driving Function (HAD-F) Evidence of Highly Automated Driving Function Release Test Evaluation Test Execution Test Case Derivation Processing Contribution to Safety Statement Risk Test Results Evaluation and Classification Test Data Test HAD-F: Proving Ground Real World Drive Test Cases Application of Test Concept incl. Variation Method 12 Space of Logical Test Cases Use Case, Knowledge, Data central decentral Data / Content Procedure Workflow Process Instruction 1 Knowledge: Laws, Standards, Guidelines,... 2 Daten: Test Drive Simulator FOT/NDS Accident Data processing Requirementsdefintion 3 Requirements Analysis Systematic Identification of 5 4 Preprocessing / Reconstruction 6 Process Guidelines + Metrics for HAD Data in PEGASUS- Format Integration Pass Criteria Logical + Parameter Space Preparation for Test Concept Application of Metrics + Mapping to Logical Source of Information Evaluation & Conversion

23 Contact: Prof. Dr..Ing. Thomas Form Head of Vehicle Technology and Mobility Experience, Group Research Volkswagen AG 23