Simulation-Based Performance Evaluation of 3D Object Detection Methods with Deep Learning for a LiDAR Point Cloud Dataset in a SOTIF-related Use Case

Milin Patel, Rolf Jung

2024

Abstract

Safety of the Intended Functionality (SOTIF) addresses sensor performance limitations and deep learning-based object detection insufficiencies to ensure the intended functionality of Automated Driving Systems (ADS). This paper presents a methodology examining the adaptability and performance evaluation of the 3D object detection methods on a LiDAR point cloud dataset generated by simulating a SOTIF-related Use Case. The major contributions of this paper include defining and modeling a SOTIF-related Use Case with 21 diverse weather conditions and generating a LiDAR point cloud dataset suitable for application of 3D object detection methods. The dataset consists of 547 frames, encompassing clear, cloudy, rainy weather conditions, corresponding to different times of the day, including noon, sunset, and night. Employing MMDetection3D and OpenPCDET toolkits, the performance of State-of-the-Art (SOTA) 3D object detection methods is evaluated and compared by testing the pre-trained Deep Learning (DL) models on the generated dataset using Average Precision (AP) and Recall metrics.

Paper Citation

in Harvard Style

Patel M. and Jung R. (2024). Simulation-Based Performance Evaluation of 3D Object Detection Methods with Deep Learning for a LiDAR Point Cloud Dataset in a SOTIF-related Use Case. In Proceedings of the 10th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS; ISBN 978-989-758-703-0, SciTePress, pages 415-426. DOI: 10.5220/0012707300003702

in Bibtex Style

@conference{vehits24,
author={Milin Patel and Rolf Jung},
title={Simulation-Based Performance Evaluation of 3D Object Detection Methods with Deep Learning for a LiDAR Point Cloud Dataset in a SOTIF-related Use Case},
booktitle={Proceedings of the 10th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS},
year={2024},
pages={415-426},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012707300003702},
isbn={978-989-758-703-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS
TI - Simulation-Based Performance Evaluation of 3D Object Detection Methods with Deep Learning for a LiDAR Point Cloud Dataset in a SOTIF-related Use Case
SN - 978-989-758-703-0
AU - Patel M.
AU - Jung R.
PY - 2024
SP - 415
EP - 426
DO - 10.5220/0012707300003702
PB - SciTePress