Open Access

ARTICLE

A Novel Ego Lanes Detection Method for Autonomous Vehicles

by Bilal Bataineh^*

Information Systems Department, College of Computers and Information Systems, Umm Al-Qura University, Mecca, 24382, Saudi Arabia

* Corresponding Author: Bilal Bataineh. Email:

Intelligent Automation & Soft Computing 2023, 37(2), 1941-1961. https://doi.org/10.32604/iasc.2023.039868

Received 21 February 2023; Accepted 12 April 2023; Issue published 21 June 2023

Abstract

Autonomous vehicles are currently regarded as an interesting topic in the AI field. For such vehicles, the lane where they are traveling should be detected. Most lane detection methods identify the whole road area with all the lanes built on it. In addition to having a low accuracy rate and slow processing time, these methods require costly hardware and training datasets, and they fail under critical conditions. In this study, a novel detection algorithm for a lane where a car is currently traveling is proposed by combining simple traditional image processing with lightweight machine learning (ML) methods. First, a preparation phase removes all unwanted information to preserve the topographical representations of virtual edges within a one-pixel width around expected lanes. Then, a simple feature extraction phase obtains only the intersection point position and angle degree of each candidate edge. Subsequently, a proposed scheme that comprises consecutive lightweight ML models is applied to detect the correct lane by using the extracted features. This scheme is based on the density-based spatial clustering of applications with noise, random forest trees, a neural network, and rule-based methods. To increase accuracy and reduce processing time, each model supports the next one during detection. When a model detects a lane, the subsequent models are skipped. The models are trained on the Karlsruhe Institute of Technology and Toyota Technological Institute datasets. Results show that the proposed method is faster and achieves higher accuracy than state-of-the-art methods. This method is simple, can handle degradation conditions, and requires low-cost hardware and training datasets.

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Keywords

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