CRADLE: An Accident Scenario Generation Method Based on Scenario Knowledge Graph Considering Accident Causation

Accident scenarios with long-tail characteristics are essential for advancing autonomous vehicles (AVs) functionality. Integrating such scenarios into the training process enhances adaptability to complex situations. However, the scarcity of accident data, due to their randomness and collection challenges, limits this integration. To address this issue, a framework named CRADLE is proposed, leveraging causal reinforcement learning (RL) and deep learning for accident scenario generation. Under extremely limited data conditions, CRADLE enables the construction of a highly diverse accident scenario database while ensuring consistency in accident causation. First, a scenario knowledge graph is constructed, incorporating both a scenario graph and an accident causation graph. Then, a scenario graph temporal prediction model trained on real driving data, generates a sampling space for scenario graphs. Subsequently, a causal inference module is developed to establish a mapping and transformation mechanism between temporal scenario graphs and accident causation graphs. Finally, a graph similarity measurement method is introduced to guide RL model in orderly sampling within the scenario graph sampling space, ensuring causally controlled scenario generation. The proposed method is applied to generate accident scenarios for two lane-changing situations: simultaneous lane changes and multi-lane changes. These scenarios are incorporated into the closed-loop self-evolution process of the autonomous driving algorithms. Experimental results demonstrate that the constructed accident scenario databases significantly improve the algorithm adaptability, reducing AV collision rates by approximately 76%.