Using in-vehicle Sensor Data for Naturalistic Driving Analysis

Transcription

1 Using in-vehicle Sensor Data for Naturalistic Driving Analysis K. Zeitouni, I. Sandu Popa (University of Versailles) G. Saint Pierre, F. Dupin, S. Glaser (LCPC-INRETS)

2 Outline Context Motivating applications Our approach Experimental results Conclusions and perspectives

3 Context Many studies on the relationship driver vehicle infrastructure ( naturalistic driving ) Understand driving behavior Qualify road infrastructure Evaluate the impact of ADAS and road installations etc Modern vehicles are natively equipped with sensors Use a data logger for data collection

4 DIRCO data logger Integrated sensors: GPS: localization Inertial station Connection to vehicle CAN bus: speed, acceleration, RPM, steering wheel, etc. Other sensors: camera, etc. Recorded data: files containing time series for each sensor What about data management?

5 Outline Context Motivating applications Our approach Experimental results Conclusions and perspectives

6 Scenarios and queries Legal speed analysis Q: Retrieve all the places where instantaneous speed is 30 km/h above the speed limit for a given percentage of the passing vehicles Relation between infrastructure and speed Q: Find all speed profiles of non constrained vehicles crossing a curve having a radius between 450 and 500m Driver behavior analysis Q: Given a route, retrieve the operating speed profile, before/after the installation of a safety radar

7 Data management Important component in the whole data-chain Huge amount of data Must consider the specificity of the data Concerns moving objects Is doubly referenced: over time and location Location can be map matched on road network No database management system (DBMS) can handle such data Prototypes for moving objects data only (no moving sensor) Secondo (Guting et. al.), (Pelakis et al.)

8 Outline Context Motivating applications Our approach Experimental results Conclusions and perspectives

9 Database system architecture Proposed model formalized as an algebra specific type set and a collection of operations Implemented as a database extension Available in SQL queries Oracle Data Server Spatio-temporal cartridge Extensibility Interface DBMS Extensions Type System Query Processing Data Indexing Database and Extensibility Services

10 Different data views Spatio-temporal trajectory (no sensor data) Temporal view: compare data from different sensors at the same moments Spatial view: compare data from the same sensor at the same locations Speed/Engine RPM Speed and Steering wheel

11 Outline Context Motivating applications Our approach Experimental results Conclusions and perspectives

12 Tested dataset Currently developing the proposed database system Dataset from a complementary study in LAVIA project 8 trips of app. 45min (47km) for two drivers Four driving styles: normal, nervous, economical and LAVIA LAVIA functioning

13 Tested queries (1/3) vehicle_trip(mo_id: int, trip:mgpoint, g_speed:greal, t_speed:mreal, g_acceleration:greal, t_acceleration:mreal, g_abs:gbool, t_abs:mbool, g_brakeswitch:gbool, g_brakeswitch:mbool, g_odometer:greal, t_odometer:mreal, g_rpm:greal, t_rpm:mreal, g_gear:gint, t_gear:mint, g_fuel:greal, t_fuel:mreal, ) Relation containing the dataset trips Query 1: How many times did the driver brake for a given trip? SELECT no_transitions(t_brakeswitch)/2 FROM vehicle_trip WHERE mo_id = &atrip; Driver Driving style Economical Normal LAVIA Nervous A B Query 1: Number of brakes for a trip

14 Tested queries (2/3) Query 2: What is the average fuel consumption for a given trip? SELECT avg(g_fuel) FROM vehicle_trip WHERE mo_id = &atrip; Driver Driving style Economical Normal LAVIA Nervous A B Query 2: Average fuel consumption for a trip (l/100km) Query 3: Compare the practiced speed for a LAVIA drive and a normal drive with the speed limit for a given route. SELECT at(g_speed, trajectory(&legalspeed)), &legalspeed FROM vehicle_trip WHERE mo_id IN (&alaviatrip, &anormaltrip);

15 Result for Query 3

16 Tested queries (3/3) Query 4: Where does the practiced speed exceed the speed limit for a given LAVIA trip? SELECT trajectory(g_speed), trajectory(greaterthan(g_speed, &legalspeed)) FROM vehicle_trip WHERE mo_id IN (&alaviatrip, &anormaltrip); Query 5: Where does the practiced speed exceed with five percent the speed limit for a given LAVIA trip? SELECT trajectory(g_speed), trajectory(greaterthan(g_speed, &legalspeed*1.05)) FROM vehicle_trip WHERE mo_id IN (&alaviatrip, &anormaltrip);

17 Query 4

18 Query 5

19 Outline Context Motivating applications Our approach Experimental results Conclusions and perspectives

20 Conclusions and perspectives Naturalistic driving studies will generate huge amounts of data Simple analysis based on file data is too costly and too slow Need a specialized database to manage and analyze such data Highly flexible and scalable operations Query and analyze the data in all dimensions Capture the continuously variability in time and space Perspectives Finalize the implementation Test on a real and large dataset