TEX-Drive: Temporal Perception Meets Experience-Guided Mixture-of-Experts for End-to-End Autonomous Driving

Human driving behavior embodies two core cognitive mechanisms: experiential reasoning and functional specialization. However, existing end-to-end autonomous driving (E2E-AD) frameworks typically model these two processes separately, often resulting in inconsistent decisions under long-horizon or dynamic conditions. To address this issue, we propose TEX-Drive, a unified E2E framework that integrates temporal perception with an experience-driven Mixture-of-Experts, achieving coordinated temporal perception–guided experience routing. Specifically, the temporal perception unit employs explicit key-frame selection to fuse critical information from both historical and current inputs, forming a temporally coherent scene representation. Building upon this, the experience-driven Mixture-of-Experts planning retrieves expert modules from a long-term memory space according to contextual similarity, dynamically routing the most relevant experts to adaptively generate future trajectories. This design enables the decision-making process to maintain temporal consistency while being guided by accumulated experiential knowledge. Extensive experiments on the Bench2Drive benchmark show that TEX-Drive consistently improves driving score, route completion, and behavioral robustness, substantially outperforming existing state-ofthe-art methods.