学習ベース車両運動計画に関する誤解との決別
Parting with Misconceptions about Learning-based Vehicle Motion Planning
nuPlanベンチマークを分析し、短期計画と長期予測は本質的に別問題であることを示した上で、センターライン選択と単純なルールベース手法を組み合わせた極めてシンプルな計画器を提案し、nuPlan 2023チャレンジで優勝した。
著者: Daniel Dauner, Marcel Hallgarten, Andreas Geiger, Kashyap Chitta
分類: cs.RO, cs.AI, cs.CV, cs.LG
原文アブストラクト
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.
