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Technical Trends of the Intelligent Connected Vehicle and Development Stage Division for Freeway Traffic Control
Technical Paper
2020-01-5134
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
It is deemed that currently the intelligent connected vehicle (ICV) is in its early stage of development, and it will go through multiple development stages in the future to realize its final goal—autonomous driving. Based on the existing ICV researches, this paper believes that ICV can be used to improve the efficiency and safety of freeway. The current research of ICV has two main directions: one focuses on the traffic flow characteristics of vehicles with different attributes, the other is concerned with using ICV to reduce congestion. From the policies issued by countries around the world and the development plans promoted by major vehicle manufacturers, the future development trends and challenges of ICV are analyzed. ICV must overcome all the shortcomings to achieve its final goal, including insufficient hardware capabilities or excessive cost, and the degree of intelligence that needs to be improved. It is concluded that in each development stage a whole new set of equipment and infrastructure is needed. Considering there are huge differences in the control measures under different ICV development stages, three principles of development stage division are proposed to make sure that the division results will be understandable and can adapt to the future traffic infrastructure construction situation. A reasonable number of stages shall reduce unnecessary waste of resources since in each stage one corresponding plan for reducing congestion is needed. Based on the existing authority division methods, the future development stage of ICV is divided into three: non-connected environment, primary connected environment and advanced connected environment.
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Xiao, Y., Guo, X., Qi, C., Dong, J. et al., "Technical Trends of the Intelligent Connected Vehicle and Development Stage Division for Freeway Traffic Control," SAE Technical Paper 2020-01-5134, 2020, https://doi.org/10.4271/2020-01-5134.Data Sets - Support Documents
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