Research on Lane Keeping System Based on Deep Learning Algorithm

With the rapid development of control technology, more and more electronic control devices are being applied to automobiles. The steer-by-wire system emerged under such a background, mainly characterized by replacing the mechanical connection between the steering wheel and the steering wheels with control signals. The Extended Kalman Filter (EKF) algorithm, as a state estimation method for nonlinear systems, can achieve accurate estimation of the system state through Kalman filtering after linearization processing. This paper introduces the basic principles of the EKF algorithm and explores its application in state estimation of automotive steer-by-wire systems. Through the establishment of the system model, algorithm implementation, and simulation experiments, the effectiveness of the EKF-based state estimation method for automotive steer-by-wire systems is verified.