分析影响3D目标检测性能的环境与物体因素，提升自动驾驶感知安全性。
Methodology for an Analysis of Influencing Factors on 3D Object Detection Performance
- 采用单变量统计分析与随机森林模型，量化环境与物体因素对检测误差的影响。
- 通过谢林值解析特征重要性，发现遮挡与距离是导致误检的主要因素。
- 适用于自动驾驶系统开发人员，帮助优化传感器融合与检测算法设计。
在自动驾驶中，目标检测对于环境感知至关重要。尽管基于深度学习的检测器表现优异，但其黑箱特性增加了安全验证的难度。本文提出一种新方法，分析与物体及环境相关的因素如何影响基于激光雷达和相机的3D目标检测器性能。通过单变量统计分析，将每个因素与行人检测误差相关联；同时，利用随机森林（Random Forest, RF）模型根据元信息预测误差，并使用谢林值（Shapley Values）解释特征重要性。该方法能捕捉特征间的依赖关系，实现对检测误差的细致分析。理解这些因素有助于揭示检测器的性能差距，推动更安全的目标检测系统研发。
原文摘要 · Abstract (English)
In automated driving, object detection is crucial for perceiving the environment. Although deep learning-based detectors offer high performance, their black-box nature complicates safety assurance. We propose a novel methodology to analyze how object- and environment-related factors affect LiDAR- and camera-based 3D object detectors. A statistical univariate analysis relates each factor to pedestrian detection errors. Additionally, a Random Forest (RF) model predicts errors from meta-information, with Shapley Values interpreting feature importance. By capturing feature dependencies, the RF enables a nuanced analysis of detection errors. Understanding these factors reveals detector performance gaps and supports safer object detection system development.
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