Performance Evaluation of Lane Detection and Tracking Algorithm Based on Learning-Based Approach for Autonomous Vehicle

Abstract

Disruptive technology, especially autonomous vehicles, is predicted to provide higher safety and reduce road traffic emissions. Lane detection and tracking are critical building blocks for developing autonomous or intelligent vehicles. This study presents a lane detecting algorithm for autonomous vehicles on different road pavements (structured and unstructured roads) to overcome challenges such as the low detection accuracy of lane detection and tracking. First, datasets for performance evaluation were created using an interpolation method. Second, a learning-based approach was used to create an algorithm using the steering angle, yaw angle, and sideslip angle as inputs for the adaptive controller. Finally, simulation tests for the lane recognition method were carried out by utilising a road driving video in Melbourne, Australia, and the BDD100K dataset created by the Berkeley DeepDrive Industrial Consortium. The mean detection accuracy ranges from 97% to 99%, and the detection time ranges from 20 to 22 ms under various road conditions with our proposed algorithm. This lane detection algorithm outperformed conventional techniques in terms of accuracy and processing time, as well as efficiency in lane detection and overcoming road interferences. The proposed algorithm will contribute to advancing the lane detection and tracking of intelligent-vehicle driving assistance and help further improve intelligent vehicle driving safety.