面向有人车引导的无人多车编队场景，设计并实现无人车在编队行驶中的车辆识别与轨迹跟踪控制系统，提出了一种多传感器后融合动目标检测算法，使用激光雷达、相机和毫米波雷达3种传感器作为数据源，分别使用欧式聚类、深度学习和运动学推理的方法对潜在目标进行检测，进而提出后融合方法将多源检测结果融合以实现对前方车辆的准确检测。基于前车轨迹生成期望路径并设计卡尔曼滤波器对期望路径进行平滑和滤波。构建车辆动力学模型、车辆道路误差模型并设计鲁棒H∞控制器进行车辆轨迹跟踪控制仿真。仿真与实车验证结果表明：在测试路段对前方车辆的平均识别准确率大于95%；实时期望路径相对于真实轨迹的均方差和轨迹平均变化率在滤波前后分别降低17.3%和48.6%；侧向控制位置误差和航向角误差相较于PID（proportional integral derivative）控制分别降低了29%和41%；车辆编队以最高54 km/h的速度实现编队整体的稳定行驶。

In the context of unmanned multi-vehicle formation guided by manned vehicles, a system for vehicle recognition and trajectory tracking control of unmanned vehicles during formation driving was devised and executed. An algorithm for multi-sensor fusion moving target detection was proposed, leveraging data from lidar, camera, and mmWave radar sensors. The algorithm utilizes Euclidean clustering, deep learning, and kinematic reasoning techniques for target detection. Additionally, a fusion methodology was introduced to integrate detection outcomes from various sources for precise identification of vehicles in the vicinity. Paths were anticipated based on the trajectories of preceding vehicles, and a Kalman filter was developed to smooth and filter these paths. A vehicle dynamic model, vehicle road error model, and the robust H∞ controller was established for vehicle trajectory tracking control simulation. Outcomes from simulation and real vehicle validation show as follows. The average recognition accuracy of preceding vehicles in test scenarios exceeds 95%. The mean squared error and average trajectory deviation rate of real-time anticipated paths decrease by 17.3% and 48.6% respectively pre and post filtering. Lateral control position error and yaw angle error decrease by 29% and 41% correspondingly compared to PID control. Vehicle formations attain stable working at speeds of up to 54 km/h.