• search hit 8 of 320
Back to Result List

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

  • 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 Lea rning (DL) models on the generated dataset usingSafety 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 Lea rning (DL) models on the generated dataset using Average Precision (AP) and Recall metrics.show moreshow less

Download full text files

Export metadata

Additional Services

Search Google Scholar
Metadaten
Author:Milin PatelORCiD, Rolf Jung
DOI:https://doi.org/10.5220/0012707300003702
Identifier:978-989-758-703-0 OPAC HS OPAC extern
Identifier:2184-495X OPAC HS OPAC extern
Parent Title (English):Proceedings of the 10th International Conference on Vehicle Technology and Intelligent Transport Systems
Publisher:SciTePress
Place of publication:Setúbal, Portugal
Document Type:conference proceeding (article)
Conference:10th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS, 02. - 04. Mai 2024, Angers France
Language:English
Year of first Publication:2024
Tag:3D Object Detection; Deep Learning; LiDAR Point Cloud; Performance Evaluation; SOTIF-related Use Case; Simulation-Based
Number of pages:12 Seiten
First Page:415
Last Page:426
Institutes:IFM - Institut für Fahrerassistenz und vernetzte Mobilität
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 60 Technik
Open Access:open_access
Research focus:FSP2: Mobilität
Publication Lists:Jung, Rolf
Patel, Milin
Publication reviewed:begutachtet
Licence (German):Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International
Release Date:2024/05/14
Verstanden ✔
Diese Webseite verwendet technisch erforderliche Session-Cookies. Durch die weitere Nutzung der Webseite stimmen Sie diesem zu. Unsere Datenschutzerklärung finden Sie hier.