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AI Empowered Communication Systems for Intelligent Transportation Systems

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2021年第7期22卷 4579-4587页

作者： Lv, Zhihan Lou, Ranran Singh, Amit Kumar Qingdao Univ Sch Data Sci & Software Engn Qingdao 266071 Peoples R China Natl Inst Technol Patna Dept Comp Sci & Engn Patna 800005 Bihar India

Intelligent control of traffic has significant influence on the scheduling efficiency of urban traffic flow. Therefore, in order to improve the efficiency of vehicles at intersections, first, the Back Propagation (BP) neural network is used to propose a vehicle passing model at the intersection, and based on the intelligent traffic control system model, the Earliest Deadline First (EDF) dynamic scheduling algorithm is used to improve the Controller Area network (CAN) communication network. Finally, the simulation test is used to evaluate the effectiveness of the proposed model and the improved CAN bus communication network. The results show that the neural network model can be used to predict the passage time of vehicles queuing at intersections with an error of less than 10%. The improved CAN bus communication can improve the data transmission rate, and the success rate of data transmission under different load rates is above 95%. In conclusion, the application of artificial intelligence technology in intelligent traffic system can improve the efficiency of vehicle scheduling and the efficiency of communication system. This research is of great significance to improve the communication performance of the transportation system and scheduling efficiency.

关键词： CAN bus BP neural network EDF algorithm intersection neural network model load rate

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