CorrRisk-WM: Corridor-Conditioned Risk World Modeling for Safety-Critical Trajectory Planning

2026-09-15

AI Summary

为解决安全关键轨迹规划问题，提出CorrRisk-WM模型，通过环境演化与监督式入侵和险情预测相结合的方法评估不同候选轨迹的风险。

Abstract

Safe local planning requires forecasting surrounding-agent motion and evaluating candidate-specific risks, since identical agent motion can pose different risks to different ego trajectories. We present CorrRisk-WM, a planning-oriented partial world model coupling environment evolution with supervised intrusion and near-miss prediction over bounded candidate-trajectory corridors. A latent environment model recursively predicts agent states and updates agent-agent and agent-map interactions. Each candidate queries the evolving environment through footprint- aware geometry and learned agent-corridor representations. A lightweight recurrent risk module uses temporal context to estimate per-slice hazards; survival aggregation yields first-entry and horizon-level event probabilities. On 29,176 scenarios from 100 Waymo validation shards, CorrRisk-WM achieves intrusion average precision (AP) of 0.8567 and 1-m near-miss first-entry AP of 0.8671. In baseline comparisons, it attains the highest near-miss AP at all three distance thresholds and the lowest observed open-loop collision rate (4.88%), with route progress of 15.35 m. Across three seeds, removing dynamic environment modeling or candidate-conditioned geometric interaction reduces mean intrusion AP from 0.8590 to 0.7624 and 0.7252, respectively. These results support coupling environment evolution with candidate-conditioned geometric reasoning for risk prediction and safety-oriented candidate selection.

Problem

• trajectory planning
• risk prediction
• agent motion
• safety-critical

Innovation

• Corridor-Conditioned
• World Modeling
• Risk Prediction
• Ego Trajectories
• Agent-Environment Interaction