MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review
With autonomous driving developing in a booming stage, accurate object detection in complex scenarios attract wide attention to ensure the safety of autonomous driving. Millimeter wave (mmWave) radar and vision fusion is a mainstream solution for accurate obstacle detection. This article presents a...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/7/2542 |
| _version_ | 1797437698389049344 |
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| author | Zhiqing Wei Fengkai Zhang Shuo Chang Yangyang Liu Huici Wu Zhiyong Feng |
| author_facet | Zhiqing Wei Fengkai Zhang Shuo Chang Yangyang Liu Huici Wu Zhiyong Feng |
| author_sort | Zhiqing Wei |
| collection | DOAJ |
| description | With autonomous driving developing in a booming stage, accurate object detection in complex scenarios attract wide attention to ensure the safety of autonomous driving. Millimeter wave (mmWave) radar and vision fusion is a mainstream solution for accurate obstacle detection. This article presents a detailed survey on mmWave radar and vision fusion based obstacle detection methods. First, we introduce the tasks, evaluation criteria, and datasets of object detection for autonomous driving. The process of mmWave radar and vision fusion is then divided into three parts: sensor deployment, sensor calibration, and sensor fusion, which are reviewed comprehensively. Specifically, we classify the fusion methods into data level, decision level, and feature level fusion methods. In addition, we introduce three-dimensional(3D) object detection, the fusion of lidar and vision in autonomous driving and multimodal information fusion, which are promising for the future. Finally, we summarize this article. |
| first_indexed | 2024-03-09T11:26:22Z |
| format | Article |
| id | doaj.art-3432006424b6467fb154a9304d494189 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T11:26:22Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3432006424b6467fb154a9304d4941892023-12-01T00:00:25ZengMDPI AGSensors1424-82202022-03-01227254210.3390/s22072542MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A ReviewZhiqing Wei0Fengkai Zhang1Shuo Chang2Yangyang Liu3Huici Wu4Zhiyong Feng5Key Laboratory of Universal Wireless Communications, Ministry of Education, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaKey Laboratory of Universal Wireless Communications, Ministry of Education, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaKey Laboratory of Universal Wireless Communications, Ministry of Education, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaKey Laboratory of Universal Wireless Communications, Ministry of Education, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaNational Engineering Lab for Mobile Network Technologies, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaKey Laboratory of Universal Wireless Communications, Ministry of Education, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaWith autonomous driving developing in a booming stage, accurate object detection in complex scenarios attract wide attention to ensure the safety of autonomous driving. Millimeter wave (mmWave) radar and vision fusion is a mainstream solution for accurate obstacle detection. This article presents a detailed survey on mmWave radar and vision fusion based obstacle detection methods. First, we introduce the tasks, evaluation criteria, and datasets of object detection for autonomous driving. The process of mmWave radar and vision fusion is then divided into three parts: sensor deployment, sensor calibration, and sensor fusion, which are reviewed comprehensively. Specifically, we classify the fusion methods into data level, decision level, and feature level fusion methods. In addition, we introduce three-dimensional(3D) object detection, the fusion of lidar and vision in autonomous driving and multimodal information fusion, which are promising for the future. Finally, we summarize this article.https://www.mdpi.com/1424-8220/22/7/2542autonomous drivingradar and vision fusionradar and camera fusionobject detectiondata level fusiondecision level fusion |
| spellingShingle | Zhiqing Wei Fengkai Zhang Shuo Chang Yangyang Liu Huici Wu Zhiyong Feng MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review Sensors autonomous driving radar and vision fusion radar and camera fusion object detection data level fusion decision level fusion |
| title | MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review |
| title_full | MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review |
| title_fullStr | MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review |
| title_full_unstemmed | MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review |
| title_short | MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review |
| title_sort | mmwave radar and vision fusion for object detection in autonomous driving a review |
| topic | autonomous driving radar and vision fusion radar and camera fusion object detection data level fusion decision level fusion |
| url | https://www.mdpi.com/1424-8220/22/7/2542 |
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