School of Automation, Chongqing University, 174 Shazheng Street, Shapingba District, Chongqing, China.
Dongfeng Xiaokang Automobile Co., Ltd, No. 1 Jiujiang Avenue, Shuangfu New District, Jiangjin District, Chongqing, China.
Comput Intell Neurosci. 2022 Sep 9;2022:9318475. doi: 10.1155/2022/9318475. eCollection 2022.
Vehicle networking and autonomous driving are hot areas of scientific research today, and they complement each other and play an important role in people's intelligent travel. Intelligent driving vehicle can enhance road safety, effectively reduce traffic flow and fuel consumption, and promote the overall social development. It has great application value in urban traffic system. The traffic condition of a city directly affects the economic development of the city and the improvement of people's quality of life. As the "core" of the urban traffic network, intersections are the frequent places where traffic jams occur. Game theory, as a win-win theory, mainly solves the problem of multiperson and multi-objective with contradictory objective functions and can be used to study the optimal signal control strategy. Aiming at this problem, the potential conflict behaviors of intelligent driving vehicles when turning left at urban intersections are analyzed and a decision model is established. A long-term trajectory prediction model of straight vehicles is established based on the Gaussian process regression model (GPR) considering the vehicle motion pattern. Combined with trajectory prediction, a decision-making process (model) for intelligent driving vehicles based on conflict resolution and a multifactor driving action selection method are proposed. A coordination algorithm based on game theory is designed for conflicting vehicles. The proposed algorithm is verified by the self-developed intelligent vehicle hardware simulation platform. The simulation results show that the PID method based on digital identification and positioning makes the intelligent vehicle obtain good system step response, can improve the disturbance tracking ability of intersection turning analysis, meet the requirements of turning control system, and reduce the complexity and randomness of parameter design, which is better than the traditional fuzzy control method.
车辆联网和自动驾驶是当今科学研究的热点领域,它们相互补充,在人们的智能出行中发挥着重要作用。智能驾驶汽车可以提高道路安全,有效减少交通流量和燃料消耗,促进社会整体发展。它在城市交通系统中有很大的应用价值。城市的交通状况直接影响城市的经济发展和人民生活质量的提高。作为城市交通网络的“核心”,交叉口是交通拥堵频繁发生的地方。博弈论作为一种双赢理论,主要解决多个人和多目标的矛盾目标函数问题,可以用于研究最优信号控制策略。针对这个问题,分析了智能驾驶车辆在城市交叉口左转时的潜在冲突行为,并建立了决策模型。基于考虑车辆运动模式的高斯过程回归模型(GPR),建立了直行车的长期轨迹预测模型。结合轨迹预测,提出了一种基于冲突解决的智能驾驶车辆决策过程(模型)和多因素驾驶动作选择方法。针对冲突车辆设计了一种基于博弈论的协调算法。通过自主开发的智能车辆硬件仿真平台对所提出的算法进行了验证。仿真结果表明,基于数字识别和定位的 PID 方法使智能车辆获得了良好的系统阶跃响应,能够提高交叉口转弯分析的干扰跟踪能力,满足转弯控制系统的要求,降低参数设计的复杂性和随机性,优于传统的模糊控制方法。