A Driver-Vehicle Model for ADS Scenario-Based Testing
Journal article, 2024

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.

road traffic




Vehicle dynamics



autonomous driving

system testing



Intelligent vehicles

autonomous vehicles


Rodrigo Queiroz

University of Waterloo

Divit Sharma

University of Waterloo

Ricardo Diniz Caldas

Software Engineering 2

Krzysztof Czarnecki

University of Waterloo

Sergio García Gonzalo

Software Engineering 2

Thorsten Berger

Software Engineering 2

Patrizio Pelliccione

Gran Sasso Science Institute (GSSI)

IEEE Transactions on Intelligent Transportation Systems

1524-9050 (ISSN) 1558-0016 (eISSN)

Vol. In Press

Subject Categories

Energy Engineering

Computational Mathematics

Vehicle Engineering


Computer Systems



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