Networked Automated Vehicles (CAVs) exhibit the potential to improve safety, increase road throughput, and optimize energy efficiency and emissions in several complex traffic scenarios. A mixed integer programming (MIP) optimization method is described for performing global multi-vehicle decision and motion planning for CAV in a highly dynamic environment consisting of multiple human driven vehicles (i.e., traditional or manual vehicles) and multiple conflicting zones (e.g., confluence points and intersections). According to the method provided by the invention, the safety, high throughput and energy efficiency are ensured by solving a global multi-vehicle constraint optimization problem. The approach provides a feasible optimal time schedule for automated vehicles to traverse road segments and conflicting zones by using information from the location, speed, and destination of manual vehicles that cannot be directly controlled. Although MIP has combinatorial complexity, the proposed approach is still feasible for real-time implementation in infrastructure, such as a Mobile Edge Computer (MEC).
在若干复杂的交通场景中,网联自动车辆(CAV)显示出提高安全性、增加道路吞吐量以及优化能量效率和排放的潜力。本发明描述了一种混合整数规划(MIP)优化方法,用于在由多辆人类驾驶的车辆(即传统或手动车辆)和多个冲突区(如合流点和交叉路口)组成的高动态环境中进行CAV的全局多车辆决策和运动计划。所提出的方法通过求解全局多车辆约束优化问题,确保了安全性、高吞吐量和能量效率。该方案通过使用来自无法直接控制的手动车辆的位置、速度和目的地的信息,为自动车辆穿过路段和冲突区提供可行的最优时间调度。尽管MIP具有组合复杂性,但所提出的方案对于在诸如移动边缘计算机(MEC)之类的基础设施中实时实施仍然是可行的。
Global multi-vehicle decision system for networked automated vehicles in dynamic environment
用于动态环境中的网联自动车辆的全局多车辆决策系统
2024-03-15
Patent
Elektronische Ressource
Chinesisch
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS |
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