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首页> 外文期刊>Journal of advanced transportation >Studying the Simultaneous Effect of Autonomous Vehicles and Distracted Driving on Safety at Unsignalized Intersections
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Studying the Simultaneous Effect of Autonomous Vehicles and Distracted Driving on Safety at Unsignalized Intersections

机译:研究自主车辆同时发生,分散驾驶在无罪交叉口的安全

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摘要

Human error is one of the leading causes of accidents. Distraction, fatigue, poor visibility, speeding, and other such errors made by drivers can cause accidents. With the rapid advancements in automation technologies, transportation planners have strived to use Intelligent Transportation Systems (ITS) to minimize human error. In this study, the effect of Autonomous Vehicles (AVs) on the number of potential conflicts at two unsignalized intersections is investigated by using a microsimulation model in PTV Vissim software. For human-driven cars, the factor that is considered for calibration is driver distraction mainly caused by reading or writing text messages on a cellphone while driving. This factor can be estimated using driving simulators. In this paper, five different scenarios were defined for simulation, in addition to the primary state, according to the different market penetration rates of AVs in Vissim. Safety assessment was performed by the Surrogate Safety Assessment Model (SSAM) using Time to Collision (TTC) and Deceleration Rate to Avoid Crashes (DRAC) indicators to determine the number of accidents. It was concluded that the presence of 100% of AVs could reduce the potential for accidents by up to 93%.
机译:人为错误是事故的主要原因之一。分散,疲劳,可见性差,加速等司机所做的其他错误可能导致事故。随着自动化技术的快速进步,运输计划人员致力于使用智能交通系统(其)来最大限度地减少人为错误。在这项研究中,通过使用PTV VISSIM软件中的微仿模型来研究自主车辆(AVS)对两个无罪交叉点的潜在冲突的效果。对于人类驱动的汽车,考虑校准的因素是驾驶员分心,主要是通过在驾驶时读取或写在手机上的文本消息引起的。可以使用驾驶模拟器估计该因素。在本文中,除了在Vissim中的AVS的不同市场渗透率之外,除了主要状态之外,还定义了五种不同的场景。使用时间碰撞(TTC)和减速率来执行安全评估(SSAM),以避免崩溃(DRAC)指标来确定事故数量。得出结论是,100%的AVS的存在可以将事故的潜力降低至93%。

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