Trajectory optimization of connected and autonomous vehicles to achieve tandem intersection control

doi:10.3785/j.issn.1008-973X.2020.02.008

Journal of ZheJiang University (Engineering Science)

2020, Vol. 54

Issue (2): 275-282 DOI: 10.3785/j.issn.1008-973X.2020.02.008

Civil and Transportation Engineering

Trajectory optimization of connected and autonomous vehicles to achieve tandem intersection control

Man GUO(

),Zhen-yu MEI,Li-hui ZHANG*(

)

College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China

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Abstract

The trajectories of connected and autonomous vehicles (CAVs) were controlled to achieve the tandem arrangement of different turning movements in fully autonomous driving environment, in order to realize the tandem control of CAVs at signalized intersections to improve the flow efficiency. The trajectory control procedure mainly includes two modules. One is to separate CAVs with different turning directions along the driving direction, and the other is to distribute CAVs with the same turning directions evenly across all lanes. Given the distribution and turning information of upstream CAVs, the trajectories of CAVs can be exactly generated as follows. The evolution process of CAV relative locations in the traffic flow is determined according to a series of rules, until the CAVs with different turning directions form tandem arrangement. The time used during each step in the evolution process is calculated based on the vehicle dynamics models for both longitudinal and lateral movements. The entire trajectories for all CAVs from the very beginning that they enter the road to the end that they leave the road are then generated. Numerical examples show that tandem arrangement can be realized by controlling the trajectories of CAVs, and the trajectory computation procedure is efficient and can be applied in the real-time control of CAVs.

Key words： connected and autonomous vehicle signalized intersection tandem control trajectory control heuristic

Received: 06 August 2019 Published: 10 March 2020

CLC:

U 9

Corresponding Authors: Li-hui ZHANG E-mail: guoman@zju.edu.cn;lihuizhang@zju.edu.cn

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Cite this article:

Man GUO,Zhen-yu MEI,Li-hui ZHANG. Trajectory optimization of connected and autonomous vehicles to achieve tandem intersection control. Journal of ZheJiang University (Engineering Science), 2020, 54(2): 275-282.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2020.02.008 OR http://www.zjujournals.com/eng/Y2020/V54/I2/275

自动车轨迹优化以实现分转向车流串联交叉口控制

在全自动驾驶环境中，采用控制网联自动车（CAVs）行驶轨迹的方法来完成分转向车流的串联排列，从而实现信号交叉口处车流的串联控制以提高通行效率. 自动车行驶轨迹控制过程主要包括：不同转向车流在纵向行驶方向上实现前后分离、同一转向车辆在所有车道上实现横向均匀分布. 给定上游来车分布及转向信息，自动车轨迹精确计算过程如下：通过一系列规则确定各自动车在交通流中的相对位置演变过程，直到分转向车流串联排列成型；基于车辆纵横向动力学模型，计算车辆相对位置演变过程中每一步所需时间；从初始状态开始整合形成每辆自动车的行驶轨迹. 算例表明，通过自动车的轨迹控制可以实现分转向车流的串联排列，且轨迹计算速度较快，可以用于车流实时控制.

关键词： 网联自动车, 信号控制交叉口, 车流串联控制, 轨迹控制, 启发式算法

Fig.1 Relative distribution of vehicles

Fig.2 Determination of free-driving vehicles

Fig.3 Schematic diagram of vehicle forward moving process

Fig.4 Example of lane changing vacancy in row with obstruction vehicle

Fig.5 Example of no lane changing vacancy in row with obstruction vehicle

Fig.6 Schematic diagram of even distribution process of vehicles

Fig.7 Speed curves for forward moving process of vehicles

Fig.8 Initial inflow vehicle distribution in a two-node network

Tab.1 Longitudinal locations of CAVs after flow mergence m

Fig.9 Evolution process of CAV relative locations

Tab.2 Longitudinal locations of CAVs in Fig. 9（b） m

Tab.3 Longitudinal locations of CAVs in Fig. 9（c） m

Tab.4 Longitudinal locations of CAVs in Fig. 9（g） m

Tab.5 Longitudinal locations of CAVs in Fig. 9（h） m

Fig.10 Final tandem arrangement of CAVs

Fig.11 Full trajectories of all vehicles in Fig. 9

Fig.12 Initial vehicle distribution with different numbers of lanes

Fig.13 Final tandem arrangement with different numbers of lanes

Fig.14 Full trajectories of all vehicles in Fig. 12


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