  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 (i.e., 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.
