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      Application-Oriented Performance Comparison of 802.11p and LTE-V in a V2V Communication System

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          Abstract

          In recent years, the Vehicle-to-Vehicle (V2V) communication system has been considered one of the most promising technologies to build a much safer and more efficient transportation system. Both simulation and field test have been extensively performed to evaluate the performance of the V2V communication system. However, most of the evaluation methods are communication-based, and although in a transportation environment, lack a V2V application-oriented analysis. In this study, we conducted real-world tests and built an application-oriented evaluation model. The experiments were classified into four scenarios: static, following, face 2 face, and crossing vertically, which almost covered all the V2V communication patterns on the road. Under these scenarios, we conducted experiments and built a probability model to evaluate the performance of 802.11p and LTE-V in safety-related applications. Consequently, we found out that improvements are still needed in Non-Line-of-Sight scenarios.

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          Author and article information

          Journal
          TST
          Tsinghua Science and Technology
          Tsinghua University Press (Xueyan Building, Tsinghua University, Beijing 100084, China )
          1007-0214
          05 April 2019
          : 24
          : 2
          : 123-133
          Affiliations
          ∙ Mengkai Shi, Yi Zhang, Danya Yao, and Chang Lu are with Department of Automation, Tsinghua University National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing 100084, China. E-mail: smk13@ 123456mails.tsinghua.edu.cn ; yaody@ 123456mail.tsinghua.edu.cn ; luchang13@ 123456mails.tsinghua.edu.cn .
          ∙ Yi Zhang is also with Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Nanjing 210096, and Tsinghua-Berkeley Shenzhen Institute (TBSI), Shenzhen 518055, China.
          Author notes
          * To whom correspondence should be addressed. E-mail: zhyi@ 123456mail.tsinghua.edu.cn .

          Mengkai Shi is currently a PhD candidate of Tsinghua University, China. He received the BEng degree from Tsinghua University in 2013. He has participated several research projects granted from MOST, NSFC, and some companies. His research interests include intelligent transportation system, intelligent vehicle-infrastructure cooperation systems, Vehicular Ad hoc Networks (VANET), connected vehicles, and cooperative driving.

          Chang Lu is currently a master student of Department of Automation, Tsinghua University. She received the BEng degree from Tsinghua University in 2016. Her recent research interests include intelligent transportation system, wireless communication in V2X system, and performance evaluation of vehicular networks.

          Yi Zhang received the BEng degree in 1986 and MEng degree in 1988 both from Tsinghua University, China, and earned the PhD degree in 1995 from University of Strathclyde in UK. He is a professor in control science and engineering at Tsinghua University with his current research interests focusing on intelligent transportation systems. Active research areas include intelligent vehicle-infrastructure cooperative systems, analysis of urban transportation systems, urban road network management, traffic data fusion and dissemination, and urban traffic control and management. His research fields also cover the advanced control theory and applications, advanced detection and measurement, and systems engineering.

          Danya Yao received the BEng, MEng, and PhD degrees from Tsinghua University in 1988, 1990, and 1994, respectively. He is a professor in control science and engineering at Tsinghua University with his current research interests focusing on intelligent transportation systems. His active areas include intelligent vehicle-infrastructure cooperative systems, connected vehicles, cooperative driving, high accuracy positioning, advanced driver assistant system, cooperative driver assistant system, traffic behaviour analysis and modeling, advanced detection and measurement, cooperative traffic control and optimization, and distributed sensor networks.

          Article
          1007-0214-24-2-123
          10.26599/TST.2018.9010075

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