提出可实时调整的动态风险评估框架，提升自动驾驶安全性。
Context-aware Risk Assessment and Its Application in Autonomous Driving
- 基于物体运动与空间关系，构建方向感知的风险量化模型。
- 碰撞率降低19%，每公里事故减少20%，驾驶评分提升17%。
- 适合高风险场景下的自动驾驶系统，计算开销极低。
确保自动驾驶安全需要精确、实时的风险评估与自适应行为。以往研究或输出粗粒度全局指标缺乏可解释性，或提出指标未真正集成到系统中，或仅聚焦特定驾驶场景。本文提出上下文感知风险指数（CRI），一种轻量级模块化框架，基于物体运动学和空间关系量化方向性风险，并实时动态调整控制指令。CRI采用基于责任敏感安全（RSS）原则的动态安全包络内方向感知空间划分，混合概率-最大值融合策略进行风险聚合，以及自适应控制策略实现行为实时调节。我们在包含220个高危场景的Bench2Drive基准上，使用先进的端到端模型Transfuser++在复杂路线上进行评估。结果显示，失败路线中的碰撞率降低19%（p=0.003），每公里碰撞减少20%（p=0.004），综合驾驶得分提升17%（p=0.016），惩罚分显著下降（p=0.013），且单次决策延迟仅3.6毫秒。结果表明，CRI在复杂高风险环境中显著提升系统安全性与鲁棒性，同时保持模块化与极低运行开销。
原文摘要 · Abstract (English)
Ensuring safety in autonomous driving requires precise, real-time risk assessment and adaptive behavior. Prior work on risk estimation either outputs coarse, global scene-level metrics lacking interpretability, proposes indicators without concrete integration into autonomous systems, or focuses narrowly on specific driving scenarios. We introduce the Context-aware Risk Index (CRI), a light-weight modular framework that quantifies directional risks based on object kinematics and spatial relationships, dynamically adjusting control commands in real time. CRI employs direction-aware spatial partitioning within a dynamic safety envelope using Responsibility-Sensitive Safety (RSS) principles, a hybrid probabilistic-max fusion strategy for risk aggregation, and an adaptive control policy for real-time behavior modulation. We evaluate CRI on the Bench2Drive benchmark comprising 220 safety-critical scenarios using a state-of-the-art end-to-end model Transfuser++ on challenging routes. Our collision-rate metrics show a 19\% reduction (p = 0.003) in vehicle collisions per failed route, a 20\% reduction (p = 0.004) in collisions per kilometer, a 17\% increase (p = 0.016) in composed driving score, and a statistically significant reduction in penalty scores (p = 0.013) with very low overhead (3.6 ms per decision cycle). These results demonstrate that CRI substantially improves safety and robustness in complex, risk-intensive environments while maintaining modularity and low runtime overhead.
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