# Methodology for an Analysis of Influencing Factors on 3D Object Detection Performance

Source: [Methodology for an Analysis of Influencing Factors on 3D Object Detection Performance](<https://www.sota2.com/research/paper/68fa79b81e209131cea7f147>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T05:04:38.175Z

Authors: Anton Kuznietsov, Dirk Schweickard, Steven Peters

Published: 2024-11-13T10:01:33.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2411.08482>)

[PDF](<https://arxiv.org/pdf/2411.08482.pdf>)