Trajectrack: Intelligent Trajectory Estimation, Speed Analysis, and Lane Detection for Autonomous Vehicles

Autonomous vehicles (AVs) need complex perception systems for safe operation under dynamic traffic scenes. We introduce TrajecTrack, a machine learning-based platform that integrates real-time trajectory estimation, velocity estimation and lane detection from LiDAR and vision inputs. We apply DBSCAN clustering and the constant velocity model for predicted trajectories, with our speed estimation based on YOLOv8 and ByteTrack, plus a new module for lane detection based on edge detection and the Hough transform. Compared to the NuScenes dataset and sample video input, TrajecTrack provides high-accuracy visualizations of the trajectories, velocities and road lane markings and therefore improves the situational awareness of AVs. This paper contributes significantly to the field of AV perception in that it supports a scalable single solution, with future implications being in traffic violative detection.