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 this https URL

基于模仿的驾驶规划者在最近几年取得了相当大的成功。然而，由于缺乏标准化的基准，各种设计的有效性仍然不清楚。新发布的 nuPlan 解决了这个问题，提供了一个大规模的真实世界数据集和一个标准化的闭环基准，以便进行公平比较。利用这个平台，我们对两个基本但尚未充分探索的方面进行了全面研究：自我规划的基本特征和减少复合误差的有效数据增强技术。此外，我们还强调了当前学习系统忽视的模仿差距。最后，结合我们的发现，我们提出了一个强大的基准模型 - PlanTF。我们的结果表明，一个经过精心设计的纯模仿规划者可以在与包含手工规则的最先进方法相比具有高度竞争力的性能，并在长尾案例中展示出卓越的泛化能力。我们的模型和基准可供公开使用。项目网站：https://this URL

自主驾驶中基于模仿的规划器的再思考

Rethinking Imitation-based Planner for Autonomous Driving

The release of nuPlan marks a new era in vehicle motion planning research,
offering the first large-scale real-world dataset and evaluation schemes
requiring both precise short-term planning and long-horizon ego-forecasting.
Existing systems struggle to simultaneously meet both requirements. Indeed, we
find that these tasks are fundamentally misaligned and should be addressed
independently. We further assess the current state of closed-loop planning in
the field, revealing the limitations of learning-based methods in complex
real-world scenarios and the value of simple rule-based priors such as
centerline selection through lane graph search algorithms. More surprisingly,
for the open-loop sub-task, we observe that the best results are achieved when
using only this centerline as scene context (\ie, ignoring all information
regarding the map and other agents). Combining these insights, we propose an
extremely simple and efficient planner which outperforms an extensive set of
competitors, winning the nuPlan planning challenge 2023.

nuPlan 提供了第一个大规模实际数据集和评估计划，对于需要精确的短期规划和长期本体预测，现有系统难以同时满足这两个要求，我们提出了一种基于规则的简单而高效的策划方法，从而在 nuPlan 计划挑战中赢得了胜利。