Autonomous target detection and tracking based on dynamic trajectory correction

Within urban traffic scenarios, the detection and tracking challenges faced by autonomous vehicles often stem from target occlusion. Traditional object detection algorithms struggle to achieve precise recognition, posing significant challenges to the safe operation of autonomous vehicles. To address this issue, this study employs multi-sensor fusion technology. By integrating data from different sensors, it aims to obtain more comprehensive information. Context-related information is utilized to thoroughly understand the logical structure of the scene. A trajectory prediction algorithm anticipates target movement paths in advance. These approaches significantly enhance the adaptability of target detection and tracking algorithms in complex environments. Research results confirm that this study achieves improved accuracy and robustness in autonomous vehicle target detection and tracking algorithms, providing reliable support for the dependable implementation of autonomous driving technology.