城市郊区道路跟车条件下智能网联汽车速度规划

Speed Planning of Intelligent and Connected Vehicle Under Following Conditions of Suburban Road Scenarios

随着智能网联汽车技术的快速发展,跟车行驶控制能够有效实现车辆智能跟随及快速高效队列行驶。针对城市郊区道路条件下的智能网联汽车速度规划问题,以提高车辆的燃油经济性、舒适性及安全性为目的,基于跟车速度限幅和车辆动力系统信息,设计了基于初值优化的序列二次规划算法(Sequential Quadratic Programming,SQP),实时求解获取车辆跟车过程中的最优速度轨迹。首先,在车联网环境下,基于车车(Vehicle to Vehicle,V2V)通信及车辆与交通设施(Vehicle to Infrastructure,V2I)通信技术实时获取前方车辆的速度、加速度及位置等行驶信息并实时采集道路交通信息;然后,为减少车辆动态能耗损失和减小所需牵引力,并在规定的时间段内完成相应的行驶路程,利用采集到的前车行驶信息,采用基于初值优化的SQP算法对最优目标车速进行求解;此外,基于周边动态的道路交通场景,考虑边界约束条件,采用滚动时域的方法实现目标车辆速度在每个采样时刻的在线滚动优化,保证目标车辆节能安全地跟车行驶;最后,通过仿真验证了该算法的有效性和实时性。研究结果表明:基于初值优化的SQP算法能够较快求解经济性较优的跟车车速轨迹,可保证车辆的行驶安全性和良好的跟车性能,减少车辆跟车过程中不必要的速度波动,最终实现跟车车辆较好的燃油经济性和行驶舒适性。

With the rapid development of intelligent and connected vehicle technology,intelligent following and efficient queue driving of vehicles can be effectively achieved through following driving control.This study focuses on the speed planning problem for intelligent and connected vehicles in urban and suburban road conditions to improve the fuel economy,comfort,and safety of the vehicles.To achieve this,based on the following speed limit and vehicle powertrain system information,an initial value optimization-based sequential quadratic programming(SQP)algorithm based on initial value optimization was designed to dynamically determine the optimal speed trajectory of vehicles during the following process.In this research,real-time driving data,such as the information of the speed,acceleration,and the position of the front vehicles,were obtained through vehicle-to-vehicle communication and vehicle-to-infrastructure communication technology within the vehicle network environment.Furthermore,real-time road traffic information was collected.Based on the collected front-following information,the SQP algorithm based on initial value optimization was used to determine the optimal target speed.The aim was to reduce the dynamic energy consumption loss,minimize the required traction force,and travel the corresponding driving distance within a specified time interval.Additionally,boundary constraint conditions based on the dynamic road traffic scene were considered.A rolling time-domain method was used to realize online rolling optimization of the target vehicle speed at each sampling moment,ensuring energy saving and safety while driving the target vehicle.Finally,the effectiveness and real-time performance of the algorithm were validated through simulations.The results show that the SQP algorithm based on initial value optimization can quickly determine the economically optimal speed trajectory for the following vehicle,indicating good following performance.This approach ensures driving safety and reduces unnecessary speed fluctuations during the following process,ultimately achieving improved fuel economy and driving comfort for the following vehicles.