Vehicle Trajectory-Tracking Model for AV using LiDAR in Snowy Weather Under Different Snowing Environments

Autonomous cars are frequently fitted with radars, cameras, and LiDAR due to their complimentary capacities of environment awareness. However, when the host vehicle's LiDAR is negatively impacted by challenging weather circumstances like snow in varied snowfall conditions, precisely following the trajectory of the previous vehicle becomes imperative .Due to the sparse nature of LiDAR data, which can be influenced by a variety of variables such as the wind or snow conditions, it is also increasingly difficult to precisely remove the snow while maintaining the point clouds' details. The complete, learning-based strategy put out in this study attempts to address this urgent issue.Intensity and spatial-temporal feature-based de-snowing, QLGMM, weighted technique, and switch RNN with a dual-level long short-term memory (LSTM) are used to track the trajectory path in snowy weather for various snowing levels.