LSTM-Based Trajectory Tracking Control for Autonomous Vehicles

With the improvement of sensor accuracy, sensor data plays an increasingly important role in intelligent vehicle motion control. Good use of sensor data can improve the control of vehicles. However, data-based end-to-end control has the disadvantages of poorly interpreted control models and high time costs; model-based control methods often have difficulties designing high-fidelity vehicle controllers because of model errors and uncertainties in building vehicle dynamics models. In the face of high-speed steering conditions, vehicle control is difficult to ensure stability and safety. Therefore, this paper proposes a hybrid model and data-driven control method. Based on the vehicle state data and road information data provided by vehicle sensors, the method constructs a deep neural network based on LSTM and Attention, which is used as a compensator to solve the performance degradation of the LQR controller due to modeling errors. The compensator takes a multidimensional sequence of vehicle state information and road information as input and outputs the compensation of steering wheel angle, which serves as feedforward and feedback. The simulation results show that the proposed control architecture can ensure the rapid convergence of the vehicle state to the steady-state and reduce the vehicle oscillation when facing high-speed steering conditions. The simulation results also show that the proposed control architecture has a smaller yaw rate and lateral acceleration, which highlights its importance in vehicle stability control.