Data-Driven Risk Fields for Safer End-to-End Autonomous Driving

2026-09-09

AI Summary

为解决自动驾驶模型缺乏风险意识的问题，本文提出DRiF框架，通过学习相对风险而非绝对分数来提高驾驶安全性。

Abstract

Safety is a fundamental requirement for autonomous driving, yet existing end-to-end driving models still lack explicit risk-aware learning capacities. Existing rule-based risk models provide interpretable safety priors, yet their absolute risk scores depend on handcrafted functions, coefficients, and thresholds. Learning-based risk representations reduce part of this manual design, but their supervision often relies on occupancy-derived labels or heuristic cost values, which may not capture ego-conditioned planning risk. In this paper, we propose DRiF, a data-driven risk-field framework for safer end-to-end autonomous driving. DRiF learns a shared BEV feature with static map segmentation, dynamic risk prediction, and vehicle planning. For dynamic risk learning, DRiF converts rule-based safety priors into pairwise risk labels, and trains the risk field to preserve relative risk ordering instead of regressing handcrafted absolute scores. Experiments on Bench2Drive show that DRiF achieves competitive overall performance, with consistent improvements in driving score, success rate, and collision-related metrics. These results establish relative risk supervision as an effective way to connect explicit safety structure with end-to-end planning. The data and code will be publicly available.

Problem

• end-to-end autonomous driving
• risk-aware learning
• rule-based risk models
• learning-based risk representations
• relative risk supervision

Innovation

• data-driven risk field
• relative risk supervision
• end-to-end autonomous driving
• dynamic risk learning