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.

翻译：开发有效的评估指标对于准确、快速地衡量激光雷达感知性能至关重要。当前存在的一个主要问题是缺乏能够基于目标检测或点云数据同时生成快速且准确评估的指标。在本研究中，我们提出了一种基于车辆栅格占据概率的新型激光雷达感知熵度量。该指标反映了点云分布对车辆检测性能的影响。基于此，我们还引入了一种激光雷达部署优化模型，该模型采用基于差分进化的粒子群优化算法进行求解。对比实验表明，在评估激光雷达感知性能时，所提出的PE-VGOP指标与车辆检测真实值之间的相关性超过0.98。此外，现场实验表明，与基础部署方案相比，所提出的优化模型能显著提升包括RS-16、RS-32和RS-80在内的多种类型激光雷达的感知能力。值得注意的是，该模型使RS-32激光雷达的检测召回率提升了25%。