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Collaborative processing and data optimization of environmental perception technologies for autonomous vehicles

Haina Song (Department of School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China)
Shengpei Zhou (Shenyang Institute of Automation (Guangzhou) Chinese Academy of Sciences, Guangzhou, China)
Zhenting Chang (Guangzhou Public Transport Group Co., Ltd, Guangzhou, China)
Yuejiang Su (Department of School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, China)
Xiaosong Liu (Guangdong Zhongke Zhenheng Information Technology Co., Ltd, Foshan, China)
Jingfeng Yang (Department of School of Electronics and Communication Engineering, SUN YAT-SEN University, Guangzhou, China and Shenyang Institute of Automation (Guangzhou) Chinese Academy of Sciences, Guangzhou, China)

Assembly Automation

ISSN: 0144-5154

Article publication date: 5 May 2021

Issue publication date: 22 July 2021




Autonomous driving depends on the collection, processing and analysis of environmental information and vehicle information. Environmental perception and processing are important prerequisite for the safety of self-driving of vehicles; it involves road boundary detection, vehicle detection, pedestrian detection using sensors such as laser rangefinder, video camera, vehicle borne radar, etc.


Subjected to various environmental factors, the data clock information is often out of sync because of different data acquisition frequency, which leads to the difficulty in data fusion. In this study, according to practical requirements, a multi-sensor environmental perception collaborative method was first proposed; then, based on the principle of target priority, large-scale priority, moving target priority and difference priority, a multi-sensor data fusion optimization algorithm based on convolutional neural network was proposed.


The average unload scheduling delay of the algorithm for test data before and after optimization under different network transmission rates. It can be seen that with the improvement of network transmission rate and processing capacity, the unload scheduling delay decreased after optimization and the performance of the test results is the closest to the optimal solution indicating the excellent performance of the optimization algorithm and its adaptivity to different environments.


In this paper, the results showed that the proposed method significantly improved the redundancy and fault tolerance of the system thus ensuring fast and correct decision-making during driving.



This research was funded by 2018 Industrial Internet innovation and development project – Basic Standards and experimental verification of industrial internet edge computing, the National Key Research and Development Program (No. 2018YFB2003500, 2018YFB1700200), Foshan entrepreneurship and innovation team project (2017IT100032). The authors would like to thank several anonymous reviewers and readers in China and abroad who gave valuable comments and suggestions.


Song, H., Zhou, S., Chang, Z., Su, Y., Liu, X. and Yang, J. (2021), "Collaborative processing and data optimization of environmental perception technologies for autonomous vehicles", Assembly Automation, Vol. 41 No. 3, pp. 283-291.



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