PI's research interests include AI-based, noise-tolerant  Lidar system design, image process and there related application issues, e.g., object-segment. Currently, we focus on a reliable LiDAR system implemented in an embedded system. /ppAutonomous vehicles rely on LiDAR (Light Detection and Ranging) technology to navigate and perceive their surroundings. However, LiDAR data can be significantly affected by adverse weather conditions such as heavy rain, snow, fog, or dust storms. These weather conditions can lead to reduced visibility and accuracy in the LiDAR data, posing a significant challenge to the safe and efficient operation of autonomous vehicles. To address this issue, the problem statement focuses on developing a system or algorithm to enhance autonomous vehicle LiDAR data by filtering out the adverse effects of weather conditions and improving the reliability and safety of autonomous driving in challenging environmental conditions. These challenges and goals not only present opportunities for innovation but also align with our project's overarching mission to create a cutting-edgebrsolution in the field of semantic segmentation./pp1. To increase the efficiency and accuracy of the existing system.br2. To execute the algorithm using embedded system.br3. To reduce computational complexity./ppFinally, your work needs can be well demonstrated in a real-environment (e.g. heavy raining condition) /p