Evaluation of 3D Vulnerable Objects’ Detection Using a Multi-Sensors System for Autonomous Vehicles
One of the primary tasks undertaken by autonomous vehicles (AVs) is object detection, which comes ahead of object tracking, trajectory estimation, and collision avoidance. Vulnerable road objects (e.g., pedestrians, cyclists, etc.) pose a greater challenge to the reliability of object detection oper...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/4/1663 |
| _version_ | 1827652765776609280 |
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| author | Esraa Khatab Ahmed Onsy Ahmed Abouelfarag |
| author_facet | Esraa Khatab Ahmed Onsy Ahmed Abouelfarag |
| author_sort | Esraa Khatab |
| collection | DOAJ |
| description | One of the primary tasks undertaken by autonomous vehicles (AVs) is object detection, which comes ahead of object tracking, trajectory estimation, and collision avoidance. Vulnerable road objects (e.g., pedestrians, cyclists, etc.) pose a greater challenge to the reliability of object detection operations due to their continuously changing behavior. The majority of commercially available AVs, and research into them, depends on employing expensive sensors. However, this hinders the development of further research on the operations of AVs. In this paper, therefore, we focus on the use of a lower-cost single-beam LiDAR in addition to a monocular camera to achieve multiple 3D vulnerable object detection in real driving scenarios, all the while maintaining real-time performance. This research also addresses the problems faced during object detection, such as the complex interaction between objects where occlusion and truncation occur, and the dynamic changes in the perspective and scale of bounding boxes. The video-processing module works upon a deep-learning detector (YOLOv3), while the LiDAR measurements are pre-processed and grouped into clusters. The output of the proposed system is objects classification and localization by having bounding boxes accompanied by a third depth dimension acquired by the LiDAR. Real-time tests show that the system can efficiently detect the 3D location of vulnerable objects in real-time scenarios. |
| first_indexed | 2024-03-09T21:04:47Z |
| format | Article |
| id | doaj.art-000780cd301342bebe330afc43c18a1e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T21:04:47Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-000780cd301342bebe330afc43c18a1e2023-11-23T22:03:15ZengMDPI AGSensors1424-82202022-02-01224166310.3390/s22041663Evaluation of 3D Vulnerable Objects’ Detection Using a Multi-Sensors System for Autonomous VehiclesEsraa Khatab0Ahmed Onsy1Ahmed Abouelfarag2Arab Academy for Science, Technology and Maritime Transport, Alexandria 1029, EgyptSchool of Engineering, University of Central Lancashire, Preston PR1 2HE, UKArab Academy for Science, Technology and Maritime Transport, Alexandria 1029, EgyptOne of the primary tasks undertaken by autonomous vehicles (AVs) is object detection, which comes ahead of object tracking, trajectory estimation, and collision avoidance. Vulnerable road objects (e.g., pedestrians, cyclists, etc.) pose a greater challenge to the reliability of object detection operations due to their continuously changing behavior. The majority of commercially available AVs, and research into them, depends on employing expensive sensors. However, this hinders the development of further research on the operations of AVs. In this paper, therefore, we focus on the use of a lower-cost single-beam LiDAR in addition to a monocular camera to achieve multiple 3D vulnerable object detection in real driving scenarios, all the while maintaining real-time performance. This research also addresses the problems faced during object detection, such as the complex interaction between objects where occlusion and truncation occur, and the dynamic changes in the perspective and scale of bounding boxes. The video-processing module works upon a deep-learning detector (YOLOv3), while the LiDAR measurements are pre-processed and grouped into clusters. The output of the proposed system is objects classification and localization by having bounding boxes accompanied by a third depth dimension acquired by the LiDAR. Real-time tests show that the system can efficiently detect the 3D location of vulnerable objects in real-time scenarios.https://www.mdpi.com/1424-8220/22/4/1663autonomous drivingmultiple object detection2D LiDARsensor fusion |
| spellingShingle | Esraa Khatab Ahmed Onsy Ahmed Abouelfarag Evaluation of 3D Vulnerable Objects’ Detection Using a Multi-Sensors System for Autonomous Vehicles Sensors autonomous driving multiple object detection 2D LiDAR sensor fusion |
| title | Evaluation of 3D Vulnerable Objects’ Detection Using a Multi-Sensors System for Autonomous Vehicles |
| title_full | Evaluation of 3D Vulnerable Objects’ Detection Using a Multi-Sensors System for Autonomous Vehicles |
| title_fullStr | Evaluation of 3D Vulnerable Objects’ Detection Using a Multi-Sensors System for Autonomous Vehicles |
| title_full_unstemmed | Evaluation of 3D Vulnerable Objects’ Detection Using a Multi-Sensors System for Autonomous Vehicles |
| title_short | Evaluation of 3D Vulnerable Objects’ Detection Using a Multi-Sensors System for Autonomous Vehicles |
| title_sort | evaluation of 3d vulnerable objects detection using a multi sensors system for autonomous vehicles |
| topic | autonomous driving multiple object detection 2D LiDAR sensor fusion |
| url | https://www.mdpi.com/1424-8220/22/4/1663 |
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