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Dynamic Eco-Driving Speed Guidance at Signalized Intersections: Multivehicle Driving Simulator Based Experimental Study

机译:信号交叉口的动态生态行驶速度引导:基于多车辆驾驶模拟器的实验研究

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Variations in vehicle fuel consumption and gas emissions are usually associated with changes in cruise speed and the aggressiveness of drivers’ acceleration/deceleration, especially at traffic signals. In an attempt to enhance vehicle fuel efficiency on arterials, this study developed a dynamic eco-driving speed guidance strategy (DESGS) using real-time signal timing and vehicle positioning information in a connected vehicle (CV) environment. DESGS mainly aims to optimize the fuel/emission speed profiles for vehicles approaching signalized intersections. An optimization-based rolling horizon and a dynamic programming approach were proposed to track the optimal guided velocity for individual vehicles along the travel segment. In addition, a vehicle specific power (VSP) based approach was integrated into DESGS to estimate the fuel consumption and CO2 emissions. To evaluate the effectiveness of the overall strategy, 15 experienced drivers were recruited to participate in interactive speed guidance experiments using multivehicle driving simulators. It was found that compared to vehicles without speed guidance, those with DESGS had a significantly reduced number of stops and approximately 25% less fuel consumption and CO2 emissions.
机译:车辆燃油消耗和气体排放的变化通常与巡航速度的变化以及驾驶员加速/减速的积极性有关,尤其是在交通信号灯时。为了提高车辆在动脉上的燃油效率,本研究开发了一种动态的生态驾驶速度引导策略(DESGS),该策略使用实时信号定时和连接车辆(CV)环境中的车辆定位信息。 DESGS的主要目的是优化接近信号交叉口的车辆的燃油/排放速度曲线。提出了一种基于优化的滚动视野和动态规划方法,以跟踪沿行驶路段的单个车辆的最佳引导速度。此外,基于车辆特定功率(VSP)的方法已集成到DESGS中,以估计燃料消耗和CO2排放量。为了评估总体策略的有效性,招募了15名经验丰富的驾驶员,以使用多车辆驾驶模拟器参与交互式速度引导实验。结果发现,与没有速度引导的车辆相比,具有DESGS的车辆的停车次数大大减少,燃油消耗和CO2排放量减少约25%。

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