A Route Planning and Traffic Control Model for Autonomous Intersections by Minimizing Conflict Zones

作     者：Liu, Yang Long, Kejun Wu, Wei 

作者机构：Changsha Univ Sci & Technol Hunan Key Lab Smart Roadway & Cooperat Vehicle Inf Changsha 410114 Hunan Peoples R China Chongqing Jiaotong Univ Coll Traff & Transportat Chongqing 400074 Peoples R China 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2023年第11卷

页      面：113678-113693页

核心收录：

基　　金：National Natural Science Foundation of China Changsha Science and Technology Plan Project [kq2107009] Hunan Provincial Natural Science Foundation of China [2023JJ30033] Hunan Provincial Innovation Foundation for Postgraduate [CX20210744] Changsha City Science and Technology Major Special Project [kh2301004] 

主　　题：Optimization Traffic control Planning Safety Delays Trajectory Throughput Autonomous systems Path planning Mixed integer linear programming Navigation Autonomous intersection management (AIM) route planning all-direction lanes mixed-integer linear programming (MILP) intersection 

摘      要：Most existing studies on autonomous intersection management (AIM) primarily focus on modeling and resolving conflicts between vehicles within an intersection, assuming predetermined routes of the autonomous vehicles (AVs) as exogenous inputs. Additionally, these studies presume scenarios in which AVs traverse the intersection at a constant speed without stopping. However, such scenarios are difficult to realize under heavy traffic demand. To address this issue, this study first discretized the intersection into numerous grids and proposed formulations to calculate the time at which the vehicles enter and exit a given grid at different speeds and accelerations based on the outer-boundary-projection dimension-reduction method. Thereafter, a bi-level programming model was established to optimize the route choices and traffic control schemes. The upper-level model aimed to minimize the conflicts within the intersection zones, considering the lane options for vehicles entering and exiting the intersection as the decision variable to optimize the AV routes. In addition, the lower-level model strived to minimize the delay for all upcoming vehicles. The time when a vehicle enters an intersection and whether it stops are utilized as decision variables. Based on the sliding time-window technique, the proposed model was transformed into a mixed-integer linear programming (MILP) problem, which is compiled by a mathematical programming language (AMPL) and solved by CPLEX. The numerical analysis shows that the proposed models significantly reduced the conflicts between the vehicles, and consequently, improved the space utilization of the intersection, reduced vehicle delays, and saved a significant amount of energy.