论文标题
神经规划指标的功效：对Nuscenes的PKL的荟萃分析
The efficacy of Neural Planning Metrics: A meta-analysis of PKL on nuScenes
论文作者
论文摘要
高性能对象检测系统在自主驾驶（AD）中起着至关重要的作用。该性能通常以平均平均精度进行评估，不考虑现场参与者的取向和距离，这对于安全AD很重要。它也忽略了环境环境。最近，Philion等。根据规划师的轨迹和地面途径的KL差异，提出了神经计划指标（PKL），以适应这些要求。在本文中，我们使用该神经计划指标来评分Nuscenes检测挑战的所有提交并分析结果。我们发现，尽管与MAP有些相关，但PKL度量表明与增加的交通密度，自我速度，道路曲率和相交的行为不同。最后，我们提出了扩展神经计划指标的想法。
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.
