用AI生成城市交通极端场景，自动优化自动驾驶系统安全能力。
PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment
- 结合规则过滤与大模型，按需生成安全关键交通场景
- 场景生成成功率提升8%，避障能力增强30%
- 适合自动驾驶测试与安全算法研发者使用
真实世界自动驾驶，尤其在城市环境中存在大量边缘情况，需要严格测试以确保产品安全与鲁棒性。然而，现有研究很少将对抗性场景生成与安全代理的闭环训练相结合，实现二者协同进化与相互提升。为此，本文通过规则化过滤开源数据集构建对抗行为知识库，并引入适配仿真环境的知识检索模块。采用大语言模型（LLM）融合知识、数据与对抗驱动方法，生成定制化安全关键交通场景。同时，在评估生成场景时，利用强化学习训练各类车辆的行为，从而在保持真实性的前提下，拓展场景多样性。实验表明，该框架提升了领域特定语言生成准确率12%；新生成场景变换的成功率提高8%；障碍物规避能力增强30%。完整论文详见：https://zhenhaooo.github.io/PCASim.github.io/
原文摘要 · Abstract (English)
Real-world autonomous driving, particularly in urban environments with numerous corner cases, requires rigorous testing to ensure product safety and robustness. However, few studies have explored integrating adversarial scenario generation with the training of safety agents in closed-loop testing, enabling efficient co-evolution and mutual enhancement of both. To address this challenge, an adversarial behavior knowledge repository is constructed by applying rule-based filtering to an open-source dataset, combined with knowledge retrieval modules tailored for simulation environments. A large language model (LLM) is employed to integrate knowledge-, data-, and adversarial-driven approaches, generating safety-critical traffic scenarios customized to user needs. Additionally, while evaluating the generated scenarios, we employ reinforcement learning models to train the behaviors of different types of vehicles, thereby enriching scenario diversity beyond existing datasets while preserving realism. Experimental results demonstrate that the proposed framework improves the accuracy of domain-specific language generation by 12\%. Moreover, the success rate of newly generated scenario transformations increases by 8\%, while obstacle-avoidance capability is enhanced by 30\%. For the complete manuscript, please refer to: https://zhenhaooo.github.io/PCASim.github.io/
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