ニューラル計画指標の有効性：nuScenesにおけるPKLのメタ分析
The efficacy of Neural Planning Metrics: A meta-analysis of PKL on nuScenes
自動運転向けのニューラル計画指標PKLをnuScenes検出チャレンジの全提出物に適用し、mAPとの相関や交通密度・自車速度・道路曲率・交差点に対する挙動の違いを分析した。
著者: Yiluan Guo, Holger Caesar, Oscar Beijbom, Jonah Philion, Sanja Fidler
分類: cs.CV, cs.RO
原文アブストラクト
A high-performing object detection system plays a crucial role in autonomous driving (AD). The performance, typically evaluated in terms of mean Average Precision, does not take into account orientation and distance of the actors in the scene, which are important for the safe AD. It also ignores environmental context. Recently, Philion et al. proposed a neural planning metric (PKL), based on the KL divergence of a planner's trajectory and the groundtruth route, to accommodate these requirements. In this paper, we use this neural planning metric to score all submissions of the nuScenes detection challenge and analyze the results. We find that while somewhat correlated with mAP, the PKL metric shows different behavior to increased traffic density, ego velocity, road curvature and intersections. Finally, we propose ideas to extend the neural planning metric.
