In recent years, imitation-based driving planners have reported considerable success. However, due to the absence of a standardized benchmark, the effectiveness of various designs remains unclear. The newly released nuPlan addresses this issue by offering a large-scale real-world dataset and a standardized closed-loop benchmark for equitable comparisons. Utilizing this platform, we conduct a comprehensive study on two fundamental yet underexplored aspects of imitation-based planners: the essential features for ego planning and the effective data augmentation techniques to reduce compounding errors. Furthermore, we highlight an imitation gap that has been overlooked by current learning systems. Finally, integrating our findings, we propose a strong baseline model-PlanTF. Our results demonstrate that a well-designed, purely imitation-based planner can achieve highly competitive performance compared to state-of-the-art methods involving hand-crafted rules and exhibit superior generalization capabilities in long-tail cases. Our models and benchmarks are publicly available. Project website https://jchengai.github.io/planTF.

翻译：近年来，基于模仿学习的驾驶规划器取得了显著成功。然而，由于缺乏标准化基准，各种设计的有效性仍不明确。新发布的nuPlan通过提供大规模真实世界数据集和标准化闭环基准来解决这一问题，实现了公平比较。利用这一平台，我们对基于模仿学习的规划器中两个基础但尚未充分探索的方面进行了全面研究：自车规划的关键特征以及用于减少复合误差的有效数据增强技术。此外，我们指出了当前学习系统所忽视的模仿差距。最后，整合我们的研究发现，我们提出了一个强大的基线模型——PlanTF。我们的结果表明，一个设计良好的纯模仿学习规划器能够与涉及手工规则的最新方法实现高度竞争性的性能，并在长尾场景中展现出卓越的泛化能力。我们的模型和基准已公开可用。项目网站：https://jchengai.github.io/planTF。