Artificial Intelligence Based Traffic Control for Edge Computing Assisted Vehicle Networks

Songlin Chen,
Hong Wen,
Jinsong Wu,

Abstract


Edge computing supported vehicle networks have attracted considerable attention in recent years both from industry and academia due to their extensive applications in urban traffic control systems. We present a general overview of Artificial Intelligence (AI)-based traffic control approaches which focuses mainly on dynamic traffic control via edge computing devices. A collaborative edge computing network embedded in the AI-based traffic control system is proposed to process the massive data from roadside sensors to shorten the real-time response time, which supports efficient traffic control and maximizes the utilization of computing resources in terms of incident levels associated with different rescue schemes. Furthermore, several open research issues and indicated future directions are discussed.

Keywords


Vehicle networks, Edge computing, Traffic control, Artificial intelligence (AI), Real-time responses

Citation Format:
Songlin Chen, Hong Wen, Jinsong Wu, "Artificial Intelligence Based Traffic Control for Edge Computing Assisted Vehicle Networks," Journal of Internet Technology, vol. 23, no. 5 , pp. 989-996, Sep. 2022.

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Published by Executive Committee, Taiwan Academic Network, Ministry of Education, Taipei, Taiwan, R.O.C
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