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

arXiv: https://arxiv.org/abs/2010.09350 · HTML: https://mlsanity.com/paper/2010.09350/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2020.

## Built on by (most cited first, 7 of 7)

- [NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles](https://mlsanity.com/paper/2106.11810/) (2021)
- [M2BEV: Multi-Camera Joint 3D Detection and Segmentation with Unified Birds-Eye View Representation](https://mlsanity.com/paper/2204.05088/) (2022)
- [Towards Neuro-Symbolic Video Understanding](https://mlsanity.com/paper/2403.11021/) (2024)
- [A Review of Testing Object-Based Environment Perception for Safe Automated Driving](https://mlsanity.com/paper/2102.08460/) (2021)
- [A Challenge to Build Neuro-Symbolic Video Agents](https://mlsanity.com/paper/2505.13851/) (2025)
- [nuScenes Revisited: Progress and Challenges in Autonomous Driving](https://mlsanity.com/paper/2512.02448/) (2025)
- [What's Hidden Matters: Identifying Planning-Critical Occluded Agents using Vision-Language Models](https://mlsanity.com/paper/2607.00283/) (2026)

## Abstract (arXiv)

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.