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ORIGINAL RESEARCH article

Front. Mech. Eng.
Sec. Mechatronics
Volume 11 - 2025 | doi: 10.3389/fmech.2025.1436338
This article is part of the Research Topic Global Excellence in Mechatronics: North America View all 4 articles

Computer Vision Model Based Robust Lane Detection using Multiple Model Adaptive Estimation Methodology

Provisionally accepted
  • 1 MicroVision (United States), Redmond, United States
  • 2 Purdue School of Engineering and Technology, Indiana University, Purdue University Indianapolis, Indianapolis, Indiana, United States

The final, formatted version of the article will be published soon.

    ABSTRACT Lane-keeping systems are a major part of advanced driver assistance systems (ADAS). Existing lane detection algorithms are based on either Computer Vision (CV) models or deep learning techniques which are often vulnerable to unfamiliar routes, lane marking conditions, night-time driving, weather conditions, etc. To improve lane detection accuracy under various challenging conditions, we propose a framework that utilizes several lane detection models with different features to obtain a robust algorithm. The proposed Multiple Model Adaptive Estimation (MMAE) algorithm works with two cameras, one front camera and one rear camera. The front camera is used for lane offset estimates whereas the rear camera serves as a time-delayed reference for the estimated lane offsets. The offsets from front camera CV models (two) are used as inputs to the MMAE algorithm which compares the offset computed by the rear camera CV model (time-delayed) as the reference. The proposed MMAE algorithm then estimates the probability of lane offsets to match the time-delayed reference model lane offset and selects the offset with higher probability of matching with reference model. The offset from the time-delayed reference model cannot be used for the real-time lane keeping control system since it would produce erroneous steering output due to the time lag in offset estimated by the real camera model. Thus, the MMAE estimated offset offers a more accurate lane offset and hence used in a PID steering controller for the lane keeping system. The proposed algorithm is then deployed in an AirSim simulation environment for performance evaluation. The simulation results show that the proposed MMAE algorithm performed robustly even when one of the models performed poorly. The proposed lane detection algorithm was able to identify the poorly performing model and switch to the other model to ensure better lane detection performance.

    Keywords: Lane-Keeping Assist Systems (LKAS), Computer vision (CV), Kalman filter, Multiple model adaptive estimation (MMAE), Lane detection (LD)

    Received: 21 May 2024; Accepted: 15 Jan 2025.

    Copyright: © 2025 Fakhari and Anwar. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Iman Fakhari, MicroVision (United States), Redmond, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.