Research on Intelligent Driving Target Tracking Technology Based on Multi-Sensor Fusion Algorithm

This paper focuses on the target tracking problem in the field of intelligent driving and proposes a solution based on a multi-sensor fusion algorithm. First, by preprocessing and calibrating data from multiple sensors—including radar, cameras, and LiDAR—the system achieves spatiotemporal synchronization of the data. Then, by integrating methods such as Kalman filtering, extended Kalman filtering, and particle filtering, an efficient and robust data fusion algorithm is designed and an appropriate system framework is constructed. Subsequently, experiments based on simulation platforms and on-road test environments are conducted to validate the proposed algorithm. The results indicate that the method has significant advantages in terms of target detection and tracking accuracy, real-time performance, and system stability. Finally, the paper discusses current issues and outlines future research directions.