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 the challenging driving scenarios from the CARLA leaderboard 2.0, which involve high-speed driving and lane-changing, and propose a rule-based expert policy to control the vehicle and generate ground truth labels for its reasoning process across different driving aspects and the final decisions. This dataset can serve as an open-loop end-to-end driving benchmark, enabling the evaluation of accuracy in various chain-of-thought aspects and the final decision. In addition, we propose a baseline model called DriveCoT-Agent, trained on our dataset, to generate chain-of-thought predictions and final decisions. The trained model exhibits strong performance in both open-loop and closed-loop evaluations, demonstrating the effectiveness of our proposed dataset.

翻译：近年来，端到端驾驶取得了显著进展，展现出系统简化以及在开环和闭环设置下均具竞争力的驾驶性能等优势。然而，其驾驶决策缺乏可解释性和可控性，这阻碍了端到端驾驶系统在实际场景中的部署。本文利用CARLA仿真器收集了一个全面的端到端驾驶数据集，命名为DriveCoT。该数据集包含传感器数据、控制决策以及用于指示推理过程的思维链标签。我们采用了CARLA排行榜2.0中的挑战性驾驶场景（涉及高速驾驶和变道），并提出了一种基于规则的专家策略来控制车辆，同时为其在不同驾驶方面的推理过程及最终决策生成真值标签。该数据集可作为开环端到端驾驶的基准，支持对多个思维链方面的准确性和最终决策进行评估。此外，我们提出了一个基于该数据集训练的基线模型DriveCoT-Agent，用于生成思维链预测和最终决策。训练后的模型在开环和闭环评估中均表现出色，证明了我们提出的数据集的有效性。