SAB: Stacking Action Blocks for Efficiently Generating Diverse Multimodal Critical Driving Scenario

Exploring critical scenarios for autonomous driving is a challenging task that requires effectively generating diverse multimodal scenarios from an infinite parameter space. However, most search-based methods encounter the mode collapse problem, leading to the generation of similar concrete scenarios for a given logical scenario. Besides, they require training different models for potentially diverse logical scenarios, which consume considerable time. To tackle these challenges, we propose the Stacked Action Blocks (SAB) framework inspired by reusable modularity in software architecture. In this framework, we extract atomic actions from logical scenarios and train each atomic action using a multimodal model as an action block to mitigate the impact of mode collapse. By reusing these trained action blocks, we compose diverse critical scenarios, thereby reducing training costs. We extensively evaluated our approach in four complex driving scenarios and achieved superior performance in the Critical Scenario Generation (CSG) task compared to heuristic methods and search-based methods. We demonstrate that the independent training of these action blocks that are reusable and modular is an effective way to find diverse multimodal critical driving scenarios. In addition, we show that the driving policy becomes better at avoiding collisions after fine-tuning based on these critical scenarios.