Generation of Critical Interactive Scenarios for Trajectory Planning

Abstract

Autonomous Vehicles (AVs) must be thoroughly tested to meet high safety requirements. Scenario-based testing using simulation is a common approach for validating them. Usually, scenarios test only one ego vehicle against road users with pre-defined behaviors called Non-Playable Characters (NPC). Such scenarios ensure reproducibility but are not always relevant and realistic, as they do not capture interactions between (e.g., non-cooperative) AVs. Consequently, they are unsuitable for testing safety-critical emerging behaviors like those happening in the real world. To tackle this problem, we propose TIAV, an approach for generating interactive critical scenarios that allows developers to study how AVs influence each other. Experiments on the reference CommonRoad simulation framework show that TIAV can identify scenarios leading to collisions and disengagements and trigger significantly more failures than a random baseline. Thanks to its ability to expose unsafe AV interactions, TIAV allows developers to validate AVs' functional correctness and check the effects of AVs' simultaneous deployment. TIAV is available as open-source software: https://github.comJparcaini/TIAV