PriorEye: Geospatial Visual Priors for End-to-End Autonomous Driving

Most end-to-end autonomous driving methods rely solely oninstantaneous sensor observations, limiting them to reactive behaviorwithout the anticipatory foresight human drivers employ through priorexperience. We introduce geospatial visual priors, street-level visual con-text anchored to the intended driving route, providing visual-spatialforesight independent of real-time sensors. We propose a memory aug-mentation module featuring a dual-memory architecture and an adap-tive memory gate, which can be easily integrated into existing end-to-end approaches. This design pairs a contextual memory for retrievedpriors with a persistent fallback memory, and dynamically regulatesthe influence of memories based on current state compatibility. Eval-uated on the NAVSIM-v2 benchmark, our approach consistently im-proves performance across diverse end-to-end baselines. Furthermore,because these priors are independent of onboard sensors, our method in-herently improves robustness against sensor corruption, while the dual-memory design ensures safe fallback when the retrieved priors them-selves become unreliable. Our project page is available at https://ori-mrg.github.io/PriorEye.