车-车协同下无人驾驶车辆的换道汇入控制方法

多车协同驾驶是智能车路系统领域的研究热点之一，可有效降低道路交通控制管理的复杂程度，减少环境污染的同时保障道路交通安全。基于多车协同驾驶控制结构，提出了一种无人驾驶车辆换道汇入的驾驶模型及策略，系统分析了多车协同运行状态的稳定条件。在综合分析无人驾驶车辆换道汇入的协作准则、安全性评估后，基于高阶多项式方法，结合车辆运行特性，通过引入乘坐舒适性的指标函数，设计得到无人驾驶车辆换道汇入的有效运动轨迹。通过研究汇入车辆与车队中汇入点前、后各车辆的运动关系，详细分析车辆发生碰撞的类型和影响因素，给出避免碰撞的条件准则，从而确保无人驾驶车辆汇入过程中多车行驶的安全性和稳定性。基于车辆运动学建立车辆位置误差模型，结合系统大范围渐进稳定的条件，选取线速度和角速度作为输入，应用李雅普诺夫稳定性理论和Backstepping非线性控制算法，设计了无人驾驶车辆换道汇入后的路径跟踪控制器。仿真试验和实车试验结果表明：所设计的换道汇入路径是可行、安全的，控制器具有良好的跟踪效果，纵向和横向的距离误差在15 cm以内，方向偏差的相对误差在10%以内。研究结果为智能车路系统中的多车状态变迁与协同驾驶研究提供了参考，可服务于未来道路交通安全设计和评价。

Lane Change Merging Control Method for Unmanned Vehicle Under V2V Cooperative Environment

Collaborative driving for the state transition of a vehicle platoon has become a popular research topic in the field of intelligent vehicle highway systems. It can effectively simplify the complexity of road traffic control management, reduce environmental pollution, and ensure road traffic safety. Based on a multi-intelligent vehicle cooperative driving control structure, this paper proposes a driving strategy for intelligent vehicles merging into a platoon, and analyzes the stable conditions for the driving state within a platoon of vehicles. After establishing the collaboration criteria and a safety assessment for when an unmanned vehicle merges into a vehicle platoon, based on high-order polynomials, the combined vehicle characteristics and effective motion trajectory for the lane changing of an unmanned vehicle are designed and obtained by introducing the index function of riding comfort. To assure vehicle safety and stability for the merging process of an unmanned vehicle, the motion relationship of each vehicle in the merging scenario was explored, including the movement relation between the merging vehicle and the front and rear vehicles of the platoon. The types and impact factors for a collision are analyzed, and the conditions for collision avoidance are provided. Based on the vehicle kinematics, a vehicle position error model is established, and the line speed and angular velocity are selected as inputs, combining the conditions of the system's large and progressive stability, as well as the path tracking controllers for the unmanned vehicle platoon merging process designed using the Lyapunov theory and Backstepping algorithm. Simulation and vehicle experiments show that the trajectories designed for merging into a vehicle platoon are feasible and safe. In addition, the controller demonstrates a better tracking performance. The longitudinal and lateral errors are within 15 cm, and the relative error of the direction deviation is less than 10%. This provides a useful reference for the unmanned state transition of a vehicle platoon and collaborative driving in an intelligent vehicle highway system, and can assist with the design and evaluation of road traffic safety in the future.