Objects Perceptibility Prediction Model Based on Machine Learning for V2I Communication Load Reduction

Lv, Pin; He, Yuebin; Han, Jinlei; Xu, Jia

doi:10.1007/978-3-030-86137-7_55

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…Compared to single-vehicle information fusion, cooperative perception multi-sensor fusion in the IoV environment has more abundant information sources, wider viewing angles, stronger adaptability, and can achieve higher perception accuracy and range [ 5 , 12 ]. Machine learning, as the mainstream technology of target detection, predicts and improves the perceptibility of the target by training the model, realizing the selective transmission of target perception information with the vehicle–road coordination device [ 9 , 13 ]. However, the existing research on cooperative perception information fusion methods is still in the stage of rapid development, and there are still deficiencies in real environment test applications and target detection research.…”

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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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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show abstract

“…In this case, it is necessary for the roadside infrastructure to predict whether the objects are perceptible to the CAVs. In [6], we propose a neural network model to predict the perceptibility of objects. Based on the model, the roadside infrastructure broadcasts the unperceived information to autonomous vehicles selectively.…”

Section: Introductionmentioning

confidence: 99%

Object Perceptibility Prediction for Transmission Load Reduction in Vehicle-Infrastructure Cooperative Perception

Han

et al. 2022

Sensors

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Vehicle-infrastructure cooperative perception is an ingenious way to eliminate environmental perception blind areas of connected and autonomous vehicles (CAVs). However, if the infrastructure transmits all environmental information to the nearby CAVs, the transmission load is so heavy that it causes a waste of network resources, such as time and bandwidth, because parts of the information are redundant for the CAVs. It is an efficient manner for the infrastructure to merely transmit the information about objects which cannot be perceived by the CAVs. Therefore, the infrastructure needs to predict whether an object is perceptible for a CAV. In this paper, a machine-leaning-based model is established to settle this problem, and a data filter is also designed to enhance the prediction accuracy in various scenarios. Based on the proposed model, the infrastructure transmits the environmental information selectively, which significantly reduces the transmission load. The experiments prove that the prediction accuracy of the model achieves up to 95%, and the transmission load is reduced by 55%.

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State Estimation and Motion Prediction of Vehicles and Vulnerable Road Users for Cooperative Autonomous Driving: A Survey

Ghorai

Eskandarian

Kim

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Objects Perceptibility Prediction Model Based on Machine Learning for V2I Communication Load Reduction

Cited by 3 publications

References 9 publications

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

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

Object Perceptibility Prediction for Transmission Load Reduction in Vehicle-Infrastructure Cooperative Perception

State Estimation and Motion Prediction of Vehicles and Vulnerable Road Users for Cooperative Autonomous Driving: A Survey

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