用真实车祸数据生成可控风险场景，提升自动驾驶测试效率。
Controllable risk scenario generation from human crash data for autonomous vehicle testing
- 构建分离正常与高危行为的潜在空间，统一建模交通参与者
- 在有限车祸数据下实现高保真、平滑的风险状态转移
- 适合需要针对性测试自动驾驶安全性的研究者使用
确保自动驾驶车辆（AV）安全需在日常驾驶和罕见高危场景下进行严格测试。核心挑战在于模拟背景车辆（BVs）和易受伤道路使用者（VRUs）在常规交通中表现真实，同时具备与真实事故一致的风险行为。本文提出可控风险代理生成框架（CRAG），统一建模主流正常行为与罕见安全关键行为。CRAG构建结构化潜在空间，解耦正常与风险相关行为，高效利用有限的车祸数据。通过结合风险感知的潜在表示与基于优化的模式转换机制，使代理能在长时间内平滑、合理地从安全状态转向风险状态，同时保持两种状态下的高保真度。大量实验表明，相比现有基线方法，CRAG提升了行为多样性，并支持可控生成风险场景，实现对自动驾驶系统鲁棒性的靶向高效评估。
原文摘要 · Abstract (English)
Ensuring the safety of autonomous vehicles (AV) requires rigorous testing under both everyday driving and rare, safety-critical conditions. A key challenge lies in simulating environment agents, including background vehicles (BVs) and vulnerable road users (VRUs), that behave realistically in nominal traffic while also exhibiting risk-prone behaviors consistent with real-world accidents. We introduce Controllable Risk Agent Generation (CRAG), a framework designed to unify the modeling of dominant nominal behaviors and rare safety-critical behaviors. CRAG constructs a structured latent space that disentangles normal and risk-related behaviors, enabling efficient use of limited crash data. By combining risk-aware latent representations with optimization-based mode-transition mechanisms, the framework allows agents to shift smoothly and plausibly from safe to risk states over extended horizons, while maintaining high fidelity in both regimes. Extensive experiments show that CRAG improves diversity compared to existing baselines, while also enabling controllable generation of risk scenarios for targeted and efficient evaluation of AV robustness.
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