Autonomous and Connected Cars: HCM Estimates for Freeways with Various Market Penetration Rates

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• 作者：Liang Shi^a ; ^{lshi@hawaii.edu" class="auth_mail" title="E-mail the corresponding author} ; Panos Prevedouros^b ; ^{pdp@hawaii.edu" class="auth_mail" title="E-mail the corresponding author}
• 关键词：HCM ; Freeway ; Capacity ; Driverless Car ; Autonomous Vehicle ; Connected Vehicle
• 刊名：Transportation Research Procedia
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：15
• 期：Complete
• 页码：389-402
• 全文大小：349 K

文摘

Major IT companies and vehicle manufacturers have announced their plans for autonomous driving technology. Autonomous light duty vehicles are often referred to as “driverless cars” (DLC). These technologies intend to partly or fully replace driving by combining navigation systems, artificial intelligence, in-vehicle sensors, roadside ITS and traffic monitoring data, vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. The absence of perception errors and the minimal perception and reaction time of DLC enable them to maintain shorter headways, to apply consistent acceleration and deceleration rates, and to optimize the use of gaps. In theory, freeway operations and level of service (LOS) can be impacted to a substantial but yet unknown degree by the DLC. The Highway Capacity Manual (HCM).is a static and macroscopic methodology for assessing various traffic flow facilities. Its 2010 edition is not adjusted for the presence of DLC on highways, however, several of its parameters are sensitive to differences in perception and reaction times, headways, etc. An investigation was conducted to assess the likely changes to traffic flow characteristics that may result from the introduction of DLC. The focus of this paper is on expressways, that is, on HCM analyses of basic freeway segment and freeway weaving segment. DLC is able to increase capacity (c), the maximum service flow rate (MSFi), the adjustment factor for unfamiliar driver population (fP), passenger-car equivalent (PCE), and the proportion of heavy vehicles (PT). The combined benefits improve LOS. The results from the case studies show that the impacts of DLC on HCM parameters are tiny if the DLC have a very small market share. Two types of DLC are considered in this paper: Autonomous DLC, and Connected DLC with V2V and V2I. On a basic freeway segment the Autonomous DLC improves LOS from D to C when its share in traffic reaches 7%. The same case study shows that the Connected DLC improves LOS from D to C when its share in traffic reaches 3%.