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.

翻译：我们提出MC-Risk，这是一种规划器对齐的鸟瞰视角网格多组件风险场，能够实现早期、校准且类别感知的风险定位。MC-Risk线性组合了三个可解释模块：(i) 机动车风险场，融合黑箱多模态轨迹预测器与分析型高斯环状结构，其横向宽度随速度/曲率增大而扩展，纵向高度随前视距离增加而衰减；(ii) 弱势道路使用者风险场，将各向同性行人团簇替换为与朝向和速度对齐的前向偏置各向异性核函数；(iii) 道路惩罚场，利用完整高精地图拓扑结构，施加越野惩罚及同/反向车道感知风险暴露。据我们所知，这是首次在RiskBench碰撞子集上对风险场公式进行标准化定量评估。MC-Risk在风险定位整体性能和危险早期预警指标上均取得最优结果。最后，我们通过将该风险场作为模型预测控制代价密度函数，展示了即插即用的规划接口，无需额外训练即可生成风险感知轨迹。