Abstract
Accurate vehicle positioning is a key technology affecting traffic safety and travel efficiency. High precision positioning technology combined with the internet of vehicles (IoV) can improve the positioning accuracy of human-driving vehicles (HDVs), which is well suited for practical application requirements and resources saving. In this paper, a positioning error prediction model based on deep neural network (DNN) and positioning information sharing methods are proposed for traffic scenarios where connected and autonomous vehicles (CAVs) and HDVs with different positioning capabilities coexist. The CAVs with high precision positioning capability is utilized to share positioning information for HDVs to enhance the cooperative positioning accuracy of vehicles with different positioning capabilities. Experimental results show the accuracy and timeliness of our proposal for enhancing vehicle positioning accuracy and sharing vehicle positioning information.
This research was supported in part by by the National Key Research and Development Program of China under Grant No. 2018YFB1600500, in part by the National Natural Science Foundation of China under Grant No. 62173012, U20A20155 and 52172339, in part by the Beijing Municipal Natural Science Foundation under Grant No. L191001, in part by the Newton Advanced Fellowship under Grant No. 62061130221, in part by the Project of HuNan Provinicial Science and Technology Department under Grant No. 2020SK2098 and 2020RC4048, in part by the CSUST Project under Grant No. 2019IC11.
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Zhang, A. et al. (2022). Cooperative Positioning Enhancement for HDVs and CAVs Coexisting Environment Using Deep Neural Networks. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2022. Lecture Notes in Computer Science, vol 13345. Springer, Cham. https://doi.org/10.1007/978-3-031-09726-3_11
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DOI: https://doi.org/10.1007/978-3-031-09726-3_11
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