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.

在本文中，我们收集了一个全面的端到端驾驶数据集，名为 DriveCoT，利用 CARLA 模拟器，通过传感器数据、控制决策和思维过程标签来指示推理过程。我们利用 CARLA 2.0 榜单中的具有挑战性的驾驶场景，提出了一种基于规则的专家策略来控制汽车并生成推理过程和最终决策的真实标签。此数据集可作为一个开环端到端驾驶基准，评估各种思维链路的准确性和最终决策。此外，我们提出了一个名为 DriveCoT-Agent 的基线模型，通过我们的数据集进行训练，来生成思维链路预测和最终决策。训练好的模型在开环和闭环评估中表现出很强的性能，证明了我们提出的数据集的有效性。