Ding Zhizhong, Sun Chao, Zhou Momiao, Liu Zhengqiong, Wu Congzhong
School of Computer and Information, Hefei University of Technology, Hefei 230009, China.
Sensors (Basel). 2021 Jun 9;21(12):3995. doi: 10.3390/s21123995.
Currently the research and development of autonomous driving vehicles (ADVs) mainly consider the situation whereby manual driving vehicles and ADVs run simultaneously on lanes. In order to acquire the information of the vehicle itself and the environment necessary for decision-making and controlling, the ADVs that are under development now are normally equipped with a lot of sensing units, for example, high precision global positioning systems, various types of radar, and video processing systems. Obviously, the current advanced driver assistance systems (ADAS) or ADVs still have some problems concerning high reliability of driving safety, as well as the vehicle's cost and price. It is certain, however, that in the future there will be some roads, areas or cities where all the vehicles are ADVs, i.e., without any human driving vehicles in traffic. For such scenarios, the methods of environment sensing, traffic instruction indicating, and vehicle controlling should be different from that of the situation mentioned above if the reliability of driving safety and the production cost expectation is to be improved significantly. With the anticipation that a more sophisticated vehicle ad hoc network (VANET) should be an essential transportation infrastructure for future ADV scenarios, the problem of vehicle turning control based on vehicle to everything (V2X) communication at road intersections is studied. The turning control at intersections mainly deals with three basic issues, i.e., target lane selection, trajectory planning and calculation, and vehicle controlling and tracking. In this paper, control strategy, model and algorithms are proposed for the three basic problems. A model predictive control (MPC) paradigm is used as the vehicle upper layer controller. Simulation is conducted on the CarSim-Simulink platform with typical intersection scenes.
目前，自动驾驶车辆（ADV）的研发主要考虑手动驾驶车辆和自动驾驶车辆在车道上同时行驶的情况。为了获取决策和控制所需的车辆自身及环境信息，目前正在研发的自动驾驶车辆通常配备了许多传感单元，例如高精度全球定位系统、各类雷达以及视频处理系统。显然，当前的先进驾驶辅助系统（ADAS）或自动驾驶车辆在驾驶安全性的高可靠性以及车辆成本和价格方面仍存在一些问题。然而，可以确定的是，未来会有一些道路、区域或城市，所有车辆均为自动驾驶车辆，即交通中没有任何手动驾驶车辆。对于这种场景，如果要显著提高驾驶安全性的可靠性和生产成本预期，环境感知、交通指令指示和车辆控制的方法应与上述情况有所不同。鉴于更复杂的车辆自组织网络（VANET）应成为未来自动驾驶场景的重要交通基础设施，研究了基于车对万物（V2X）通信的道路交叉口车辆转向控制问题。交叉口的转向控制主要涉及三个基本问题，即目标车道选择、轨迹规划与计算以及车辆控制与跟踪。本文针对这三个基本问题提出了控制策略、模型和算法。采用模型预测控制（MPC）范式作为车辆上层控制器。在具有典型交叉场景的CarSim - Simulink平台上进行了仿真。
