MC-Risk: Multi-Component Risk Fields for Risk Identification and Motion Planning

TL;DR

MC-Risk introduces a multi-component risk field for improved risk localization and hazard prediction in motion planning, combining interpretable modules for better safety and planning efficiency.

Contribution

The paper presents MC-Risk, a novel, planner-aligned risk field with three interpretable modules, and provides the first standardized evaluation on RiskBench.

Findings

MC-Risk achieves the best risk localization and hazard indication.

It enables risk-aware trajectory generation without additional training.

The approach is validated on a standardized collision subset.

Abstract

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.