Preprocessing Methods of Lane Detection and Tracking for Autonomous Driving

TL;DR

This paper surveys preprocessing techniques essential for robust, real-time lane detection and tracking in vision-based autonomous driving systems, addressing challenges like noise and lighting variations.

Contribution

It provides a comprehensive review of preprocessing methods used in lane detection and tracking for autonomous vehicles, highlighting their importance for real-time performance.

Findings

Preprocessing enhances lane detection accuracy under challenging conditions.

Robust methods improve real-time lane tracking performance.

Survey identifies key techniques and future research directions.

Abstract

In the past few years, researches on advanced driver assistance systems (ADASs) have been carried out and deployed in intelligent vehicles. Systems that have been developed can perform different tasks, such as lane keeping assistance (LKA), lane departure warning (LDW), lane change warning (LCW) and adaptive cruise control (ACC). Real time lane detection and tracking (LDT) is one of the most consequential parts to performing the above tasks. Images which are extracted from the video, contain noise and other unwanted factors such as variation in lightening, shadow from nearby objects and etc. that requires robust preprocessing methods for lane marking detection and tracking. Preprocessing is critical for the subsequent steps and real time performance because its main function is to remove the irrelevant image parts and enhance the feature of interest. In this paper, we survey preprocessing methods for detecting lane marking as well as tracking lane boundaries in real time focusing on vision-based system.