Multi-sensor fusion algorithm in cooperative vehicle-infrastructure system for blind spot warning

IF 1.9 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS International Journal of Distributed Sensor Networks Pub Date : 2022-05-01 DOI:10.1177/15501329221100412
Chao Xiang, Li Zhang, Xiaopo Xie, Longgang Zhao, Xin Ke, Zhendong Niu, Feng Wang
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引用次数: 5

Abstract

With the rapid development of electric vehicles and artificial intelligence technology, the automatic driving industry has entered a rapid development stage. However, there is a risk of traffic accidents due to the blind spot of vision, whether autonomous vehicles or traditional vehicles. In this article, a multi-sensor fusion perception method is proposed, in which the semantic information from the camera and the range information from the LiDAR are fused at the data layer and the LiDAR point cloud containing semantic information is clustered to obtain the type and location information of the objects. Based on the sensor equipments deployed on the roadside, the sensing information processed by the fusion method is sent to the nearby vehicles in real-time through 5G and V2X technology for blind spot early warning, and its feasibility is verified by experiments and simulations. The blind spot warning scheme based on roadside multi-sensor fusion perception proposed in this article has been experimentally verified in the closed park, which can obviously reduce the traffic accidents caused by the blind spot of vision, and is of great significance to improve traffic safety.
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用于盲点预警的协同车辆基础设施系统中的多传感器融合算法
随着电动汽车和人工智能技术的快速发展,自动驾驶行业进入了快速发展阶段。然而,无论是自动驾驶汽车还是传统汽车,由于视觉盲点都存在交通事故的风险。本文提出了一种多传感器融合感知方法,在数据层融合来自相机的语义信息和来自激光雷达的距离信息,并对包含语义信息的激光雷达点云进行聚类,以获得物体的类型和位置信息。基于部署在路边的传感器设备,通过5G和V2X技术将融合方法处理后的传感信息实时发送给附近车辆进行盲点预警,并通过实验和仿真验证了其可行性。本文提出的基于路边多传感器融合感知的盲点预警方案已在封闭式公园中进行了实验验证,可以明显减少由视觉盲点引起的交通事故,对提高交通安全具有重要意义。
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来源期刊
CiteScore
6.50
自引率
4.30%
发文量
94
审稿时长
3.6 months
期刊介绍: International Journal of Distributed Sensor Networks (IJDSN) is a JCR ranked, peer-reviewed, open access journal that focuses on applied research and applications of sensor networks. The goal of this journal is to provide a forum for the publication of important research contributions in developing high performance computing solutions to problems arising from the complexities of these sensor network systems. Articles highlight advances in uses of sensor network systems for solving computational tasks in manufacturing, engineering and environmental systems.
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