摘要
随着自动驾驶技术的发展,如何引导联网自动车辆(Connected and Autonomous Vehicles, CAV)安全高效行驶受到了学术界的广泛关注。为了对复杂交通环境下CAV轨迹进行优化,提出了一种有效的混合交通流下CAV的轨迹优化模型。模型将交叉口划分为可变道区域与不可变道区域。在可变道区域构建人工驾驶车辆(Human-driven Vehicles, HV)变道概率模型,以优化行驶舒适度为目标建立响应变道的协同控制策略。在不可变道区域结合车辆队列识别与交叉口信号灯配时,以快速通过交叉口为目标,提出一种标记识别的轨迹优化方法。仿真结果表明,模型在不同CAV比重下平均行驶时间、停车延误时间和舒适度都得到了显著提升。敏感性分析表明,模型最大承载车流量应设置为1 080 veh/h,有效右转车辆比重范围设置为40%以内。
With the development of automatic driving technology, how to guide the connected and autonomous vehicle(CAV) to run safely and efficiently has attracted extensive attention from the academic community. In order to optimize CAV trajectory in complex traffic environment, an effective trajectory optimization model of CAV under mixed traffic flow is proposed. The model divides the intersection into variable lane area and immutable lane area. The lane change probability model of human-driven vehicles(HV) is constructed in the variable lane area, and the cooperative control strategy in response to lane change is established with the goal of optimizing driving comfort. Combined with vehicle queue identification and intersection signal timing in the immutable lane area, a trajectory optimization method of marking recognition is proposed to quickly pass through the intersection. The simulation results show that the average driving time, parking delay time and comfort of the model have been significantly improved under different CAV proportions. The sensitivity analysis shows that the maximum load-carrying vehicle flow of the model should be set at 1 080 veh/h, and the effective right-turning vehicle proportion range should be set within 40%.
作者
张浩天
ZHANG Haotian(School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)
出处
《智能计算机与应用》
2022年第10期92-100,106,共10页
Intelligent Computer and Applications
关键词
混合交通环境
信号交叉口
联网自动车辆
轨迹优化
mixed traffic environment
signalized intersection
Connected and Autonomous Vehicles
trajectory optimization
