Kun Zhao

End-to-end autonomous driving has demonstrated remarkable potential due to its strong scene-understanding capabilities. However, its performance degrades significantly in the presence of occlusions and complex multi-agent interactions, posing serious safety risks. Existing methods struggle to understand partially observed environments and accurately predict the dynamic behaviors of surrounding agents. To address these limitations, we propose OAIAD (Occlusion-Aware Interactive End-to-End Autonomous Driving), a novel end-to-end framework designed to enhance occlusion reasoning and interaction awareness. This framework specifically addresses the critical challenge of right-of-way conflicts in complex multi-agent scenarios. OAIAD employs a stereoscopic vectorized representation to explicitly model occluded areas and incorporates a module for joint optimization of trajectory prediction and planning to better capture future agent dynamics. By explicitly modeling interactive behaviors and leveraging joint trajectory optimization, OAIAD enhances the ego vehicle’s ability to negotiate the right-of-way interactions in a safe and socially compliant manner, significantly reducing conflict-induced collisions. Extensive evaluations on both open- and closed-loop datasets demonstrate that OAIAD significantly improves performance in occlusion-heavy and interaction-intensive scenarios. Real-world experiments further validate the practicality and robustness of our approach, highlighting its potential for deployment in complex urban environments.