Research and Simulation Implementation of Self-Driving Vehicle Trajectory Optimization in Networked Intersection Environment Southeast University

Format:

Book

Conference/Event

Author/Creator:

Xu, Yuting, author.

Contributor:

Wu, Xianyu

Zhang, Yong

Conference Name:

2024 International Conference on Smart Transportation Interdisciplinary Studies (2024-12-13 : Nanjing, China)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

Nowadays, the rapidly developing Connected and Autonomous Vehicle (CAV) provides a new mode of intersection vehicle cooperative control, which can optimize vehicle trajectories and signal phases in real time and reduce intersection delays through the advantages of vehicle-road cooperative information interaction and the high controllability of CAV. In this paper, the intersection of Jintong West Road and Guanghua Road in Beijing is taken as the research object, and the vehicle trajectory constraints, acceleration constraints, speed constraints, safe driving constraints, signal switch constraints and traffic signal control constraints are set up with the minimization of traffic delay as the objective function. The DQN deep reinforcement learning network is constructed based on vehicle states, vehicle actions, reward functions, and update rules, and starts learning and updating to generate the target network. Then, SUMO software is used to simulate and test and compare the trajectory optimization process. Firstly, the road network environment is constructed and the signal light phases are set, and then the parameters of each vType and vehicle are set according to the existing data, so that the traffic simulation is as consistent as possible with the actual situation. Then we take 1s as a step, export the state data of the vehicle at each step through the TraCI interface, use the trajectory optimization model to control the acceleration of the vehicle and the state of the signal light, and feedback the control results in real time, and finally compare the indexes before and after the optimization. It is found that the trajectory optimization scheme not only improves the traffic condition of the intersection, but also reduces the average CO, CO2 emission and fuel consumption by 11.6%, 8.5% and 20.3%, respectively

Notes:

Vendor supplied data

Publisher Number:

2025-01-7194

Access Restriction:

Restricted for use by site license