Sistem Kendali Otonom pada Kendaraan Listrik Menggunakan Sensor Fusion dan Kalman Filter - Ahmad Rizal (Journal of Engineering and Technological Science, 2025)
The development of autonomous electric vehicles requires highly accurate and reliable control systems to ensure safety and efficiency. This research presents an autonomous control system for electric vehicles using sensor fusion integrated with the Kalman Filter algorithm. The system combines data from multiple sensors, including LiDAR, IMU, and GPS, to improve localization accuracy and environmental awareness. A simulation-based experiment was conducted using MATLAB/Simulink and Robot Operating System (ROS) environments. The results show that the Kalman Filter reduces localization error by 37% compared to single-sensor systems, while the sensor fusion approach improves object detection stability under dynamic conditions. The proposed system demonstrates improved path tracking accuracy and smoother control response. These findings highlight the effectiveness of sensor fusion and Kalman Filter implementation in enhancing autonomous vehicle navigation performance.