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

Parting with Misconceptions about Learning-based Vehicle Motion Planning