A Driver-Vehicle Model for ADS Scenario-Based Testing

Scenario-based testing for automated driving systems (ADS) must be able to simulate traffic scenarios that rely on interactions with other vehicles. Although many languages for high-level scenario modelling have been proposed, they lack the features to precisely and reliably control the required mic...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2024-08, Vol.25 (8), p.8641-8654
Main Authors: Queiroz, Rodrigo, Sharma, Divit, Caldas, Ricardo, Czarnecki, Krzysztof, Garcia, Sergio, Berger, Thorsten, Pelliccione, Patrizio
Format: Article
Language:English
Subjects:
autonomous driving
autonomous vehicles
DSL
Intelligent vehicles
road traffic
Roads
Scalability
simulation
system testing
Testing
Trajectory
Vehicle dynamics
Vehicles
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Summary:Scenario-based testing for automated driving systems (ADS) must be able to simulate traffic scenarios that rely on interactions with other vehicles. Although many languages for high-level scenario modelling have been proposed, they lack the features to precisely and reliably control the required micro-simulation, while also supporting behavior reuse and test reproducibility for a wide range of interactive scenarios. To fill this gap between scenario design and execution, we propose the Simulated Driver-Vehicle (SDV) model to represent and simulate vehicles as dynamic entities with their behavior being constrained by scenario design and goals set by testers. The model combines driver and vehicle as a single entity. It is based on human-like driving and the mechanical limitations of real vehicles for realistic simulation. The model leverages behavior trees to express high-level behaviors in terms of lower-level maneuvers, affording multiple driving styles and reuse. Furthermore, optimization-based maneuver planners guide the simulated vehicles towards the desired behavior. Our extensive evaluation shows the model's design effectiveness using NHTSA pre-crash scenarios, its motion realism in comparison to naturalistic urban traffic, and its scalability with traffic density. Finally, we show the applicability of our SDV model to test a real ADS and to identify crash scenarios, which are impractical to represent using predefined vehicle trajectories. The SDV model instances can be injected into existing simulation environments via co-simulation.
ISSN:1524-9050
1558-0016
1558-0016
DOI:10.1109/TITS.2024.3373531