A method of vehicle-infrastructure cooperative perception based vehicle state information fusion using improved kalman filter

Mo, Yanghui; Zhang, Peilin; Chen, Zhijun; Ran, Bin

doi:10.1007/s11042-020-10488-2

Cited by 30 publications

(14 citation statements)

References 32 publications

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“…The fusion of multiple LiDAR point clouds with spatial diversity characteristics to achieve regional cooperative perception can significantly improve the accuracy of scene perception. Cooperative perception can fuse data from multiple LiDAR sensors through V2I [ 90 , 91 , 92 ] or I2I [ 19 , 93 , 94 ], to realize information sharing and reduce sensor deployment costs. Based on the general procedure of 3D object detection, cooperative perception can be classified into three categories depending on the sensing information to be shared between LiDAR: raw, feature, and object-level cooperative perception [ 95 ].…”

Section: Object Detection Based On Roadside Lidarmentioning

confidence: 99%

Object Detection Based on Roadside LiDAR for Cooperative Driving Automation: A Review

Sun

Wang

et al. 2022

Sensors

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Light Detection and Ranging (LiDAR) technology has the advantages of high detection accuracy, a wide range of perception, and not being affected by light. The 3D LiDAR is placed at the commanding height of the traffic scene, the overall situation can be grasped from the perspective of top view, and the trajectory of each object in the traffic scene can be accurately perceived in real time, and then the object information can be distributed to the surrounding vehicles or other roadside LiDAR through advanced wireless communication equipment, which can significantly improve the local perception ability of an autonomous vehicle. This paper first describes the characteristics of roadside LiDAR and the challenges of object detection and then reviews in detail the current methods of object detection based on a single roadside LiDAR and multi-LiDAR cooperatives. Then, some studies for roadside LiDAR perception in adverse weather and datasets released in recent years are introduced. Finally, some current open challenges and future works for roadside LiDAR perception are discussed. To the best of our knowledge, this is the first work to systematically study roadside LiDAR perception methods and datasets. It has an important guiding role in further promoting the research of roadside LiDAR perception for practical applications.

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Section: Object Detection Based On Roadside Lidarmentioning

confidence: 99%

Object Detection Based on Roadside LiDAR for Cooperative Driving Automation: A Review

Sun

Wang

et al. 2022

Sensors

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“…Most V2X methods explored late fusion strategies to aggregate information from infrastructure and vehicles. For example, a late fusion two-level Kalman filter is proposed by [27] for roadside infrastructure failure conditions. Xiangmo et al .…”

Section: Introductionmentioning

confidence: 99%

V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer

Xu¹,

Xiang²,

Tu³

et al. 2022

Preprint

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In this paper, we investigate the application of Vehicle-to-Everything (V2X) communication to improve the perception performance of autonomous vehicles. We present a robust cooperative perception framework with V2X communication using a novel vision Transformer. Specifically, we build a holistic attention model, namely V2X-ViT, to effectively fuse information across on-road agents (i.e., vehicles and infrastructure). V2X-ViT consists of alternating layers of heterogeneous multi-agent self-attention and multi-scale window self-attention, which captures inter-agent interaction and per-agent spatial relationships. These key modules are designed in a unified Transformer architecture to handle common V2X challenges, including asynchronous information sharing, pose errors, and heterogeneity of V2X components. To validate our approach, we create a large-scale V2X perception dataset using CARLA and OpenCDA. Extensive experimental results demonstrate that V2X-ViT sets new state-of-the-art performance for 3D object detection and achieves robust performance even under harsh, noisy environments. The dataset, source code, and trained models will be open-sourced.

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“…In order to make up for the insufficiency of autonomous driving perception capability and data calculation above level 3, advanced sensing technology, edge computing, communication, and other technologies need to be combined to build an autonomous driving cooperative perception system in the IoV environment, enhancing perception accuracy, improving the perception range, and reducing delay. Furthermore, this will provide more accurate and rich environmental information for autonomous vehicles in real-time and lay the foundation for the realization of autonomous driving above level 3 [ 10 , 11 ]. Simultaneously, cooperative perception in the IoV environment can reduce the number of onboard sensors, lower the cost of autonomous vehicles, and speed up the commercialization of high-level autonomous driving.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Perception Technology of Autonomous Driving in the Internet of Vehicles Environment: A Review

Cui

Zhang

Xiao

et al. 2022

Sensors

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Cooperative perception, as a critical technology of intelligent connected vehicles, aims to use wireless communication technology to interact and fuse environmental information obtained by edge nodes with local perception information, which can improve vehicle perception accuracy, reduce latency, and eliminate perception blind spots. It has become a current research hotspot. Based on the analysis of the related literature on the Internet of vehicles (IoV), this paper summarizes the multi-sensor information fusion method, information sharing strategy, and communication technology of autonomous driving cooperative perception technology in the IoV environment. Firstly, cooperative perception information fusion methods, such as image fusion, point cloud fusion, and image–point cloud fusion, are summarized and compared according to the approaches of sensor information fusion. Secondly, recent research on communication technology and the sharing strategies of cooperative perception technology is summarized and analyzed in detail. Simultaneously, combined with the practical application of V2X, the influence of network communication performance on cooperative perception is analyzed, considering factors such as latency, packet loss rate, and channel congestion, and the existing research methods are discussed. Finally, based on the summary and analysis of the above studies, future research issues on cooperative perception are proposed, and the development trend of cooperative perception technology is forecast to help researchers in this field quickly understand the research status, hotspots, and prospects of cooperative perception technology.

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A method of vehicle-infrastructure cooperative perception based vehicle state information fusion using improved kalman filter

Cited by 30 publications

References 32 publications

Object Detection Based on Roadside LiDAR for Cooperative Driving Automation: A Review

Object Detection Based on Roadside LiDAR for Cooperative Driving Automation: A Review

V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer

Cooperative Perception Technology of Autonomous Driving in the Internet of Vehicles Environment: A Review

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