SoAD: Safety-Oriented Value Estimation for Enhanced Closed-Loop End-to-End Autonomous Driving

End-to-end (E2E) autonomous driving systems, which map sensory inputs directly to vehicle planning, have garnered attention for harnessing the potential of data-driven methodologies in motion planning. However, current methods face two limitations that undermine their safety performance in closed-loop driving tasks. First, the predominant imitation learning (IL) paradigm overlooks long-term safety beyond predefined planning horizons, potentially guiding the ego vehicle into hazardous states. Second, the lack of reliable online evaluation mechanisms limits real-time responses to safety risks. To overcome these challenges, we propose SoAD, a safety-oriented E2E framework that integrates long-term safety awareness into planning. SoAD is distinguished by a reinforcement learning (RL)-based value estimation module to quantify the safety of planned trajectories, and a vector world model (VWM) to generate interaction-aware future rollouts. During training, the system benefits from value-guided fine-tuning (VFT) that optimizes the planning distribution to favor safer trajectories. In closed-loop deployment, a planning rescoring (PRS) mechanism is designed to perform reliable online evaluation by combining ego-conditional predictions from the VWM with corresponding safety value estimates. Experimental results on the Bench2Drive closed-loop benchmark demonstrate the state-of-the-art (SoTA) performance of SoAD, achieving a 66.40% improvement in driving score (DS) compared to the vectorized scene representation for efficient autonomous driving (VAD) baseline, while zero-shot evaluation on the driving in occlusion simulation (DOS) benchmark further highlights its strong generalization ability.