DriveCoT: Integrating Chain-of-Thought Reasoning with End-to-End Driving

arXiv:2403.16996v1 Announce Type: new
Abstract: End-to-end driving has made significant progress in recent years, demonstrating benefits such as system simplicity and competitive driving performance under both open-loop and closed-loop settings. Nevertheless, the lack of interpretability and controllability in its driving decisions hinders real-world deployment for end-to-end driving systems. In this paper, we collect a comprehensive end-to-end driving dataset named DriveCoT, leveraging the CARLA simulator. It contains sensor data, control decisions, and chain-of-thought labels to indicate the reasoning process. We utilize …

abstract arxiv benefits cs.cv cs.ro decisions deployment driving interpretability loop paper performance progress reasoning simplicity systems thought type world