Controllable Collision Scenario Generation via Collision Pattern Prediction

• URL: http://arxiv.org/abs/2510.12206v2
• Date: Mon, 27 Oct 2025 14:53:32 GMT
• Title: Controllable Collision Scenario Generation via Collision Pattern Prediction
• Authors: Pin-Lun Chen, Chi-Hsi Kung, Che-Han Chang, Wei-Chen Chiu, Yi-Ting Chen,
• Abstract summary: We introduce controllable collision scenario generation, where the goal is to produce trajectories that realize a user-specified collision type and time-to-accident (TTA)<n>We propose a framework that predicts Collision Pattern, a compact and interpretable representation that captures the spatial configuration of the ego and the adversarial vehicles at impact.<n>Experiments show that our approach outperforms strong baselines in both collision rate and controllability.
• Score: 22.736678908526653
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Evaluating the safety of autonomous vehicles (AVs) requires diverse, safety-critical scenarios, with collisions being especially important yet rare and unsafe to collect in the real world. Therefore, the community has been focusing on generating safety-critical scenarios in simulation. However, controlling attributes such as collision type and time-to-accident (TTA) remains challenging. We introduce a new task called controllable collision scenario generation, where the goal is to produce trajectories that realize a user-specified collision type and TTA, to investigate the feasibility of automatically generating desired collision scenarios. To support this task, we present COLLIDE, a large-scale collision scenario dataset constructed by transforming real-world driving logs into diverse collisions, balanced across five representative collision types and different TTA intervals. We propose a framework that predicts Collision Pattern, a compact and interpretable representation that captures the spatial configuration of the ego and the adversarial vehicles at impact, before rolling out full adversarial trajectories. Experiments show that our approach outperforms strong baselines in both collision rate and controllability. Furthermore, generated scenarios consistently induce higher planner failure rates, revealing limitations of existing planners. We demonstrate that these scenarios fine-tune planners for robustness improvements, contributing to safer AV deployment in different collision scenarios. Project page is available at https://submit-user.github.io/anon2025

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