Deep Reinforcement Learning with Behavioral Cloning for Path Tracking of Four-Wheel Steering Vehicles

When the vehicle system performs trajectory tracking control, it presents relatively complex nonlinear coupling dynamics characteristics. The traditional coordination algorithm relying on a simplified linear model is mostly unable to deal well with the actual nonlinear dynamic behaviors. In contrast, reinforcement learning (RL) method will derive the optimal strategy by means of interaction with the environment. This eliminates the need for accurate vehicle modeling. These methods use all of the nonlinear approximation capabilities of deep neural networks and can effectively reflect the complex relationship between vehicle state and control actions. The framework itself supports multidimensional input processing and continuous operation space optimization because of the development of parallel processing architectures. In order to reduce the motion jitter caused by the direct generation of front and rear wheel angles by the network, this article uses steering angle increments as control commands and effectively reduces motion jitter through differential action formulas. To accelerate policy convergence, behavioral cloning (BC) technology was integrated into the deep reinforcement learning (DRL) training pipeline and initial policy parameterization was performed using expert demonstration data. The combination of four-wheel independent steering and DRL improves operational flexibility and enables precise trajectory compliance under different operating conditions.