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。