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arXiv · 2504.07507

Drive in Corridors: Enhancing the Safety of End-to-end Autonomous Driving via Corridor Learning and Planning

Zhiwei Zhang·Ruichen Yang·Ke Wu·Zijun Xu·Jingchu Liu·Lisen Mu·Zhongxue Gan·Wenchao Ding
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Abstract

Safety remains one of the most critical challenges in autonomous driving systems. In recent years, the end-to-end driving has shown great promise in advancing vehicle autonomy in a scalable manner. However, existing approaches often face safety risks due to the lack of explicit behavior constraints. To address this issue, we uncover a new paradigm by introducing the corridor as the intermediate representation. Widely adopted in robotics planning, the corridors represents spatio-temporal obstacle-free zones for the vehicle to traverse. To ensure accurate corridor prediction in diverse traffic scenarios, we develop a comprehensive learning pipeline including data annotation, architecture refinement and loss formulation. The predicted corridor is further integrated as the constraint in a trajectory optimization process. By extending the differentiability of the optimization, we enable the optimized trajectory to be seamlessly trained within the end-to-end learning framework, improving both safety and interpretability. Experimental results on the nuScenes dataset demonstrate state-of-the-art performance of our approach, showing a 66.7% reduction in collisions with agents and a 46.5% reduction with curbs, significantly enhancing the safety of end-to-end driving. Additionally, incorporating the corridor contributes to higher success rates in closed-loop evaluations. Project page: https://zhiwei-pg.github.io/Drive-in-Corridors.

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Zhiwei Zhang, Ruichen Yang, Ke Wu, Zijun Xu, Jingchu Liu, Lisen Mu, Zhongxue Gan, Wenchao Ding. 2025-04-10. Drive in Corridors: Enhancing the Safety of End-to-end Autonomous Driving via Corridor Learning and Planning. https://arxiv.org/abs/2504.07507

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