Hybrid tracker based optimal path tracking system for complex road environments for autonomous driving

TL;DR

This paper introduces a hybrid tracker-based optimal path tracking system for autonomous vehicles, combining deep learning lane detection and multiple path tracking algorithms to improve stability and comfort in complex road environments.

Contribution

It presents a novel hybrid tracking approach with an observer mechanism for selecting the best tracker, enhancing stability and accuracy in complex driving scenarios.

Findings

High match rate with actual lanes

Maintains vehicle within lanes accurately

Ensures stable and comfortable driving in complex environments

Abstract

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.