Automated and Complete Generation of Traffic Scenarios at Road Junctions Using a Multi-level Danger Definition
For AV certification, this provides a systematic way to generate exhaustive dangerous scenarios at junctions, but the approach is incremental as it builds on existing path-overlap concepts.
The paper proposes a method to automatically generate a complete set of dangerous traffic scenarios at road junctions by enumerating all permutations of overlapping abstract paths, enabling simulation-based testing of autonomous vehicles. Experiments show that a state-of-the-art learning-based AV controller exhibits increasing unsafe behaviors (up to 100% in some configurations) across two realistic junctions with varying numbers of actors.
To ensure their safe use, autonomous vehicles (AVs) must meet rigorous certification criteria that involve executing maneuvers safely within (arbitrary) scenarios where other actors perform their intended maneuvers. For that purpose, existing scenario generation approaches optimize search to derive scenarios with high probability of dangerous situations. In this paper, we hypothesize that at road junctions, potential danger predominantly arises from overlapping paths of individual actors carrying out their designated high-level maneuvers. As a step towards AV certification, we propose an approach to derive a complete set of (potentially dangerous) abstract scenarios at any given road junction, i.e. all permutations of overlapping abstract paths assigned to actors (including the AV) for a given set of possible abstract paths. From these abstract scenarios, we derive exact paths that actors must follow to guide simulation-based testing towards potential collisions. We conduct extensive experiments to evaluate the behavior of a state-of-the-art learning-based AV controller on scenarios generated over two realistic road junctions with increasing number of external actors. Results show that the AV-under-test is involved in increasing percentages of unsafe behaviors in simulation, which vary according to functional- and logical-level scenario properties.