Cooper: Cooperative Perception for Connected Autonomous Vehicles Based on 3D Point Clouds
Autonomous vehicles may make wrong decisions due to inaccurate detection and recognition. Therefore, an intelligent vehicle can combine its own data with that of other vehicles to enhance perceptive ability, and thus improve detection accuracy and driving safety. However, multi-vehicle cooperative p...
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| Vydané v: | Proceedings of the International Conference on Distributed Computing Systems s. 514 - 524 |
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| Hlavní autori: | , , , |
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| Jazyk: | English |
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IEEE
01.07.2019
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| ISSN: | 2575-8411 |
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| Abstract | Autonomous vehicles may make wrong decisions due to inaccurate detection and recognition. Therefore, an intelligent vehicle can combine its own data with that of other vehicles to enhance perceptive ability, and thus improve detection accuracy and driving safety. However, multi-vehicle cooperative perception requires the integration of real world scenes and the traffic of raw sensor data exchange far exceeds the bandwidth of existing vehicular networks. To the best our knowledge, we are the first to conduct a study on raw-data level cooperative perception for enhancing the detection ability of self-driving systems. In this work, relying on LiDAR 3D point clouds, we fuse the sensor data collected from different positions and angles of connected vehicles. A point cloud based 3D object detection method is proposed to work on a diversity of aligned point clouds. Experimental results on KITTI and our collected dataset show that the proposed system outperforms perception by extending sensing area, improving detection accuracy and promoting augmented results. Most importantly, we demonstrate it is possible to transmit point clouds data for cooperative perception via existing vehicular network technologies. |
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| AbstractList | Autonomous vehicles may make wrong decisions due to inaccurate detection and recognition. Therefore, an intelligent vehicle can combine its own data with that of other vehicles to enhance perceptive ability, and thus improve detection accuracy and driving safety. However, multi-vehicle cooperative perception requires the integration of real world scenes and the traffic of raw sensor data exchange far exceeds the bandwidth of existing vehicular networks. To the best our knowledge, we are the first to conduct a study on raw-data level cooperative perception for enhancing the detection ability of self-driving systems. In this work, relying on LiDAR 3D point clouds, we fuse the sensor data collected from different positions and angles of connected vehicles. A point cloud based 3D object detection method is proposed to work on a diversity of aligned point clouds. Experimental results on KITTI and our collected dataset show that the proposed system outperforms perception by extending sensing area, improving detection accuracy and promoting augmented results. Most importantly, we demonstrate it is possible to transmit point clouds data for cooperative perception via existing vehicular network technologies. |
| Author | Chen, Qi Fu, Song Yang, Qing Tang, Sihai |
| Author_xml | – sequence: 1 givenname: Qi surname: Chen fullname: Chen, Qi organization: University of North Texas – sequence: 2 givenname: Sihai surname: Tang fullname: Tang, Sihai organization: University of North Texas – sequence: 3 givenname: Qing surname: Yang fullname: Yang, Qing organization: University of North Texas – sequence: 4 givenname: Song surname: Fu fullname: Fu, Song organization: University of North Texas |
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| Snippet | Autonomous vehicles may make wrong decisions due to inaccurate detection and recognition. Therefore, an intelligent vehicle can combine its own data with that... |
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| SubjectTerms | 3D object detection Accidents Automobiles Autonomous vehicles Connected autonomous vehicles Cooperative perception Laser radar Object detection point clouds Sensors Three-dimensional displays |
| Title | Cooper: Cooperative Perception for Connected Autonomous Vehicles Based on 3D Point Clouds |
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