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Investigation of Automated Vehicle Effects on Driver's Behavior and Traffic Performance

机译:自动驾驶车辆对驾驶员行为和交通绩效的影响研究

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Advanced Driver Assistance Systems (ADAS) offer the possibility of helping drivers to fulfill their driving tasks. Automated vehicles (AV) are capable of communicating with surrounding vehicles (V2V) and infrastructure (V2I) in order to collect and provide essential information about the driving environment. Studies have proved that automated driving have the potential to decrease traffic congestion by reducing the time headway (THW), enhancing the traffic capacity and improving the safety margins in car following. Despite different encouraging factors, automated driving raise some concerns such as possible loss of situation awareness, overreliance on automation and system failure. This paper aims to investigate the effects of AV on driver’s behavior and traffic performance. A literature review was conducted to examine the AV effects on driver’s behavior. Findings from the literature survey reveal that conventional vehicles (CV), i.e. human driven, which are driving close to a platoon of AV with short THW, tend to reduce their THW and spend more time under their critical THW. Additionally, driving highly AV reduce situation awareness and can intensify driver drowsiness, exclusively in light traffic. In order to investigate the influences of AV on traffic performance, a simulation case study consisting of a 100% AV scenario and a 100% CV scenario was performed using microscopic traffic simulation. Outputs of this simulation study reveal that the positive effects of AV on roads are especially highlighted when the network is crowded (e.g. peak hours). This can definitely count as a constructive point for the future of road networks with higher demands. In details, average density of autobahn segment remarkably improved by 8.09% during p.m. peak hours in the AV scenario, while the average travel speed enhanced relatively by 8.48%. As a consequent, the average travel time improved by 9.00% in the AV scenario. The outcome of this study jointly with the previous driving simulator studies illustrates a successful practice of microscopic traffic simulation to investigate the effects of AV. However, further development of the microscopic traffic simulation models are required and further investigations of mixed traffic situation with AV and CV need to be conducted.
机译:先进的驾驶员辅助系统(ADAS)提供了帮助驾驶员完成驾驶任务的可能性。自动化车辆(AV)能够与周围的车辆(V2V)和基础设施(V2I)通信,以便收集并提供有关驾驶环境的基本信息。研究证明,自动驾驶有可能通过减少时空行驶时间(THW),增强交通通行能力和提高汽车跟随安全性来减少交通拥堵。尽管有不同的鼓励因素,自动驾驶仍引起一些问题,例如可能会失去对状况的了解,对自动化的过度依赖和系统故障。本文旨在研究AV对驾驶员行为和交通性能的影响。进行了文献综述,以检查AV对驾驶员行为的影响。文献调查的结果表明,传统的车辆(CV),即人驾驶的,在接近THW短的AV排时,往往会降低THW并在临界THW以下花费更多时间。此外,高驾驶量会降低态势感知能力,并可能加剧驾驶员的困倦感,而这种情况仅发生在轻型交通中。为了研究AV对交通性能的影响,使用微观交通模拟对100%AV情景和100%CV情景进行了模拟案例研究。这项模拟研究的结果表明,当网络拥挤时(例如高峰时段),自动驾驶对道路的积极影响尤为突出。这绝对可以算作是对路标要求更高的未来的建设性点。详细地讲,在下午期间,高速公路路段的平均密度显着提高了8.09%。在AV情景中达到高峰时段,而平均旅行速度相对提高了8.48%。因此,在AV场景中,平均旅行时间缩短了9.00%。这项研究的结果与先前的驾驶模拟器研究一起表明了微观交通模拟研究AV效果的成功实践。但是,需要进一步开发微观交通模拟模型,并且需要对带有AV和CV的混合交通情况进行进一步研究。

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