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A high-performing object detection system plays a crucial role in autonomousdriving (AD). The performance, typically evaluated in terms of mean AveragePrecision, does not take into account orientation and distance of the actors inthe scene, which are important for the safe AD. It also ignores environmentalcontext. 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 planningmetric to score all submissions of the nuScenes detection challenge and analyzethe results. We find that while somewhat correlated with mAP, the PKL metricshows different behavior to increased traffic density, ego velocity, roadcurvature and intersections. Finally, we propose ideas to extend the neuralplanning metric.

The efficacy of Neural Planning Metrics: A meta-analysis of PKL on nuScenes