Test Automation for Interactive Scenarios via Promptable Traffic Simulation
This work addresses the need for rigorous testing of autonomous vehicle planners against human behavior uncertainty, offering an incremental improvement in automated scenario generation for domain-specific applications.
The paper tackles the challenge of generating realistic and safety-critical human behaviors for autonomous vehicle planner evaluation by introducing an automated method that uses low-dimensional goal positions and a promptable traffic simulator, ProSim, to efficiently identify diverse and critical scenarios through Bayesian optimization.
Autonomous vehicle (AV) planners must undergo rigorous evaluation before widespread deployment on public roads, particularly to assess their robustness against the uncertainty of human behaviors. While recent advancements in data-driven scenario generation enable the simulation of realistic human behaviors in interactive settings, leveraging these models to construct comprehensive tests for AV planners remains an open challenge. In this work, we introduce an automated method to efficiently generate realistic and safety-critical human behaviors for AV planner evaluation in interactive scenarios. We parameterize complex human behaviors using low-dimensional goal positions, which are then fed into a promptable traffic simulator, ProSim, to guide the behaviors of simulated agents. To automate test generation, we introduce a prompt generation module that explores the goal domain and efficiently identifies safety-critical behaviors using Bayesian optimization. We apply our method to the evaluation of an optimization-based planner and demonstrate its effectiveness and efficiency in automatically generating diverse and realistic driving behaviors across scenarios with varying initial conditions.