RISE: Enhancing Safety for End-to-End Autonomous Driving through Risk-Aware Energy-Guided Diffusion Planner

In end-to-end autonomous driving, vision-language-action (VLA) planners still tend to generate the high-risk trajectories in some long-tail scenarios. Existing safety-oriented approaches rely mostly on the post-training, which requires a costly fine-tuning of the pre-trained backbone and often degrades its general driving capability, thus leading to an inevitable safety-efficiency trade-off. In this paper, we propose RISE, a RISk-aware Energy-guided planning framework that incorporates these safety constraints into the trajectory generation of a diffusion planner, without any additional fine-tuning of the pre-trained VLA backbone. Specifically, we design a mixed energy guidance consisting of a rule-based guidance, an expert-based guidance and a risk assessor. The rule-based guidance constructs differentiable energy fields from drivable-area compliance and lane-keeping constraints, while the expert-based guidance takes reference trajectories from a safety-specialized expert trained separately via a safe reinforcement learning variant. Since the expert is inherently conservative, a lightweight rule-based risk assessor is further designed to activate the expert guidance only when potential risk is detected, thus preserving driving efficiency in ordinary scenarios. Experiments show that RISE improves the ReCogDrive baseline by 1.1 PDMS on NAVSIM (91.5) and by 24.6 PDMS on its high-risk subset, and achieves a Driving Score of 85.52 with a 59.09% Success Rate on the Bench2Drive closed-loop benchmark, outperforming the baseline by 12.03 and 17.27 percentage point, respectively.