Colligen: Physics-Guided Self-Learning for Generating Collision Trajectories of Vehicles

Collision data are critical for improving the safety and robustness of autonomous driving systems. However, real collision trajectories are scarce and expensive to collect. We address this limitation by generating physically plausible collision tra- jectories using pretrained normal-driving predictors from abundant normal-driving data. We propose COLLIGEN, a physics-guided self-training framework that pro- gressively adapts a normal-driving trajectory model into a collision trajectory generator. COLLIGEN consists of two training phases. In the Pre-collision phase, the model learns trajectories that lead to impact while remaining behaviorally plausible; in the Post-collision phase, it learns trajectories that satisfy post-impact physical constraints. To guide the adaptation, we adopt Markov Chain Monte Carlo sampling with physics-based scoring to enhance collision feasibility, dynamic consistency, and motion regularity of self-generation. Experiments show that COL- LIGEN substantially outperforms strong text-to-trajectory, trajectory-to-trajectory, and trajectory-prediction baselines in reconstructing collision trajectories synthe- sized from NHTSA accident reports. Qualitative results show that COLLIGEN produces more realistic and physically plausible pre- and post-collision motion.