Application of noise filtering techniques to enhance directional stability in autonomous vehicle control

Tóm tắt

This paper presents the application of the Kalman filter (KF) for navigation and motion stabilization of autonomous vehicles, aiming to improve the accuracy of positioning systems in complex environments. The KF, a linear estimation technique, is utilized to reduce noise in sensor signals, particularly those from long-range transmission devices that are vulnerable to electromagnetic interference, which can cause signal distortion or fluctuations. By integrating data from multiple sensors, including GPS, IMU, and LiDAR, the method enables autonomous vehicles to estimate their position and orientation accurately, even under conditions of signal interruption or noise. By estimating the system state and minimizing sensor errors, the KF enhances environmental perception and ensures stable navigation. The study involves both simulation and real-world experiments with autonomous vehicles in diverse traffic scenarios to evaluate the effectiveness and accuracy of the KF in navigation stabilization and autonomous driving performance improvement.