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.

翻译：nuPlan的发布标志着车辆运动规划研究进入新时代，它提供了首个大规模真实世界数据集及评估方案，要求同时实现精确的短期规划与长时间跨度的自车预测。现有系统难以同时满足这两项要求。事实上，我们发现这些任务本质上是互不兼容的，应当独立处理。我们进一步评估了当前领域内闭环规划的现状，揭示了基于学习方法在复杂真实场景中的局限性，以及基于车道图搜索算法的简单规则先验（如中心线选择）的价值。更令人惊讶的是，对于开环子任务，我们观察到仅使用该中心线作为场景上下文（即忽略所有地图及其他智能体信息）即可取得最佳结果。综合这些见解，我们提出一种极其简单高效的规划器，其性能超越大量竞品，赢得了2023年nuPlan规划挑战赛冠军。