提出多组件风险场，实现早期精准风险定位与避障规划。
MC-Risk: Multi-Component Risk Fields for Risk Identification and Motion Planning
- 将三种可解释模块线性组合：车辆、行人、道路拓扑风险
- 在RiskBench上首次量化评估，风险定位最优且预警最早
- 可直接接入模型预测控制，无需额外训练
我们提出MC-Risk，一种鸟瞰图网格上的规划对齐多组件风险场，能实现早期、校准且类别感知的风险定位。该方法线性组合三个可解释模块：(i) 车辆风险场，融合黑箱多模态轨迹预测器与解析的高斯-环面构造，横向宽度随速度/曲率增长，高度随前瞻距离衰减；(ii) VRU风险场，用前向偏置的各向异性核替代传统各向同性行人区域，方向与速度对齐；(iii) 道路惩罚场，利用完整HD地图拓扑，施加离路惩罚及同向/对向车道的风险暴露。我们在已知的首个标准化定量评估中，在RiskBench的碰撞子集上测试了风险场方法。MC-Risk在整体风险定位上表现最佳，且最早发出危险提示。最后，我们通过将该场作为MPC代价密度，展示了即插即用的规划接口，实现风险感知轨迹生成而无需额外训练。
原文摘要 · Abstract (English)
We present MC-Risk, a planner-aligned, multi-component risk field on a bird's-eye-view grid that yields early, calibrated, and class-aware risk localization. MC-Risk linearly composes three interpretable modules: (i) a motorized-agent field that fuses a black-box multimodal trajectory predictor with an analytic Gaussian-torus construction whose lateral width grows with speed/curvature and whose height attenuates with look-ahead; (ii) a VRU risk field that replaces isotropic pedestrian blobs with a forward-biased anisotropic kernel aligned to heading and speed; and (iii) a road penalty field that exploits full HD-map topology, imposing an off-road penalty and lane-aware risk exposure for same/opposite directions. We conduct, to our knowledge, the first standardized quantitative evaluation of a risk-field formulation on RiskBench's collision subset. MC-Risk attains the best overall risk localization and the earliest hazard indication. Finally, we demonstrate a plug-and-play planning interface by using the field as an MPC cost density, enabling risk-aware trajectory generation without additional training.
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