A Novel Perception Entropy Metric for Optimizing Vehicle Perception with LiDAR Deployment

TL;DR

This paper introduces a new perception entropy metric for LiDAR that improves evaluation speed and accuracy, and an optimization model that enhances LiDAR deployment to boost vehicle detection performance.

Contribution

It proposes a novel perception entropy metric based on vehicle grid occupancy probability and an optimization model for LiDAR deployment using differential evolution and particle swarm algorithms.

Findings

PE-VGOP correlates over 0.98 with ground truth.

Deployment optimization improves detection recall by 25%.

Enhances perception capabilities of various LiDAR types.

Abstract

Developing an effective evaluation metric is crucial for accurately and swiftly measuring LiDAR perception performance. One major issue is the lack of metrics that can simultaneously generate fast and accurate evaluations based on either object detection or point cloud data. In this study, we propose a novel LiDAR perception entropy metric based on the probability of vehicle grid occupancy. This metric reflects the influence of point cloud distribution on vehicle detection performance. Based on this, we also introduce a LiDAR deployment optimization model, which is solved using a differential evolution-based particle swarm optimization algorithm. A comparative experiment demonstrated that the proposed PE-VGOP offers a correlation of more than 0.98 with vehicle detection ground truth in evaluating LiDAR perception performance. Furthermore, compared to the base deployment, field experiments indicate that the proposed optimization model can significantly enhance the perception capabilities of various types of LiDARs, including RS-16, RS-32, and RS-80. Notably, it achieves a 25% increase in detection Recall for the RS-32 LiDAR.