ADAS perception systems using point clouds struggle with noise, inconsistent data across sensors, and computational limits on embedded hardware. This work introduces a pipeline that reduces noise early, standardizes inputs from different sensors, and enables efficient segmentation and tracking suitable for both real-time embedded use and offline processing.
