ECSAS: Exploring Critical Scenarios from Action Sequence in Autonomous Driving
This addresses the bottleneck of critical scenario generation for autonomous driving testing, though it appears incremental as it builds on existing reinforcement learning and optimization techniques.
The paper tackles the problem of generating critical scenarios in autonomous driving by modeling action sequences, proposing the ECSAS framework with a description language and reinforcement learning optimizations, and shows it is more efficient than random and combination testing methods.
Critical scenario generation requires the ability of sampling critical combinations from the infinite parameter space in the logic scenario. Existing solutions aim to explore the correlation of action parameters in the initial scenario rather than action sequences. How to model action sequences so that one can further consider the effects of different action parameters in the scenario is the bottleneck of the problem. In this paper, we attack the problem by proposing the ECSAS framework. Specifically, we first propose a description language, BTScenario, allowing us to model action sequences of the scenarios. We then use reinforcement learning to search for combinations of critical action parameters. To increase efficiency, we further propose several optimizations, including action masking and replay buffer. We have implemented ECSAS, and experimental results show that it is more efficient than native approaches such as random and combination testing in various nontrivial scenarios.