  Path tracking system plays a key technology in autonomous driving. The system
should be driven accurately along the lane and be careful not to cause any
inconvenience to passengers. To address such tasks, this paper proposes hybrid
tracker based optimal path tracking system. By applying a deep learning based
lane detection algorithm and a designated fast lane fitting algorithm, this
paper developed a lane processing algorithm that shows a match rate with actual
lanes with minimal computational cost. In addition, three modified path
tracking algorithms were designed using the GPS based path or the vision based
path. In the driving system, a match rate for the correct ideal path does not
necessarily represent driving stability. This paper proposes hybrid tracker
based optimal path tracking system by applying the concept of an observer that
selects the optimal tracker appropriately in complex road environments. The
driving stability has been studied in complex road environments such as
straight road with multiple 3-way junctions, roundabouts, intersections, and
tunnels. Consequently, the proposed system experimentally showed the high
performance with consistent driving comfort by maintaining the vehicle within
the lanes accurately even in the presence of high complexity of road
conditions. Code will be available in https://github.com/DGIST-ARTIV.
