To deal with the nonlinear interference caused by chassis movement and road surface undulations with the tracking and aiming of unmanned combat ground vehicles, a tracking and aiming adaptive control method for unmanned combat ground vehicles on the move based on reinforcement learning compensation is proposed. This method consists of a main controller and a compensation controller. The main controller uses the PID control algorithm combined with the current tracking error to obtain the main control quantity, and the compensation controller uses the Dueling DQN reinforcement learning network to process the current state of the combat vehicle as well as the road surface undulation information near the local planning path to obtain the compensation control quantity. Firstly, the integrated kinematics model of the unmanned combat ground vehicle is established. Then, the compensation control algorithm based on reinforcement learning is described. Finally, simulation and verification are performed in three-dimensional scenes based on the V-REP dynamic software. The experimental results show that the tracking and aiming control method based on reinforcement learning compensation has good adaptive ability for chassis movement and road surface undulations, which effectively improves the tracking/aiming accuracy and stability of unmanned combat vehicles.

Due to the complex and dynamic character of intersection scenarios, the autonomous driving strategy at intersections has been a difficult problem and a hot point in the research of intelligent transportation systems in recent years. This paper gives a brief summary of state-of-the-art autonomous driving strategies at intersections. Firstly, we enumerate and analyze common types of intersection scenarios, corresponding simulation platforms, as well as related datasets. Secondly, by reviewing previous studies, we have summarized characteristics of existing autonomous driving strategies and classified them into several categories. Finally, we point out problems of the existing autonomous driving strategies and put forward several valuable research outlooks.