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A stochastic two-dimensional intelligent driver car-following model with vehicular dynamics
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作者 祁宏生 应雨燕 《Chinese Physics B》 SCIE EI CAS CSCD 2023年第4期430-442,共13页
The law of vehicle movement has long been studied under the umbrella of microscopic traffic flow models,especially the car-following(CF)models.These models of the movement of vehicles serve as the backbone of traffic ... The law of vehicle movement has long been studied under the umbrella of microscopic traffic flow models,especially the car-following(CF)models.These models of the movement of vehicles serve as the backbone of traffic flow analysis,simulation,autonomous vehicle development,etc.Two-dimensional(2D)vehicular movement is basically stochastic and is the result of interactions between a driver's behavior and a vehicle's characteristics.Current microscopic models either neglect 2D noise,or overlook vehicle dynamics.The modeling capabilities,thus,are limited,so that stochastic lateral movement cannot be reproduced.The present research extends an intelligent driver model(IDM)by explicitly considering both vehicle dynamics and 2D noises to formulate a stochastic 2D IDM model,with vehicle dynamics based on the stochastic differential equation(SDE)theory.Control inputs from the vehicle include the steer rate and longitudinal acceleration,both of which are developed based on an idea from a traditional intelligent driver model.The stochastic stability condition is analyzed on the basis of Lyapunov theory.Numerical analysis is used to assess the two cases:(i)when a vehicle accelerates from a standstill and(ii)when a platoon of vehicles follow a leader with a stop-and-go speed profile,the formation of congestion and subsequent dispersion are simulated.The results show that the model can reproduce the stochastic 2D trajectories of the vehicle and the marginal distribution of lateral movement.The proposed model can be used in both a simulation platform and a behavioral analysis of a human driver in traffic flow. 展开更多
关键词 intelligent model vehicular dynamics stochastic differential equation stochastic stability
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混合自动驾驶场景多换道需求下的主动间隙适配和换道序列规划 被引量:6
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作者 祁宏生 应雨燕 +1 位作者 林俊山 姚志洪 《交通运输工程与信息学报》 2021年第4期36-51,共16页
为解决城市道路拥挤状态下车辆可行换道间隙少、换道时间长,进而导致停车等待、堵塞后续车流等问题,本文基于智能网联环境下车辆之间的交互协同,提出了一种混合自动驾驶场景多换道需求下的主动间隙适配和换道序列规划模型。首先,采用多... 为解决城市道路拥挤状态下车辆可行换道间隙少、换道时间长,进而导致停车等待、堵塞后续车流等问题,本文基于智能网联环境下车辆之间的交互协同,提出了一种混合自动驾驶场景多换道需求下的主动间隙适配和换道序列规划模型。首先,采用多项式和三角函数分别描述了换道过程中的空间轨迹和速度曲线,得出了换道间隙可行性判别依据。在此基础上,构造了换道启动可行状态集合,并构建了单个换道请求下的间隙适配和协同换道的最优控制模型。然后,考虑多换道需求,构建了主动间隙适配和换道序列规划模型,对换道序列进行整体优化。最后,设计了数值仿真实验,验证了本文模型可对多换道需求进行时空优化。仿真实验结果表明,本文模型能够降低换道行为对城市道路通行能力的影响,且适用于不同的交通需求,模型可提升24%的道路通过量。 展开更多
关键词 智能交通 智能网联车辆 换道 轨迹规划 间隙 仿真
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