Advancements in lane marking detection: an extensive evaluation of current methods and future research direction
File(s)
Accepted version
Author(s)
Sheng, Shaofan
Formosa, Nicolette
Hossain, Moinul
Quddus, Mohammed
Type
Journal Article
Abstract
As the automotive industry moves towards Autonomous Vehicles (AVs), developing reliable sensing systems such as lane marking detection, is crucial. Lane markings offer essential spatial and navigational cues for AVs' safety and efficiency. Therefore, it is vital to thoroughly understand the evolution and effectiveness of different lane marking detection technologies, particularly in identifying the challenges and influencing factors for their successful implementation. To facilitate an objective comparison, a comprehensive dataset was collected from a motorway in the UK employing an instrumented vehicle. This dataset contains representative scenarios including optimal conditions, faded lane markings, adverse weather conditions, nighttime and traffic congestion. Using this dataset, a comparative analysis of four prominent lane marking detection methods such as (i) Spatial Convolutional Neural Network, (ii) Lane detection model with attention mechanism, (iii) Inverse Saliency Pyramid Reconstruction Network (InSPyReNet) and (iv) REcurrent Feature-Shift Aggregator was conducted. InSPyReNet technique emerged superior, demonstrating outstanding precision and sensitivity in lane detection. The consistent evaluation approach in this research contributes to identifying the most suitable technique for robust lane marking detection under various environmental conditions. The study also navigates future research paths, emphasising model generalisation, and the importance of robustness in challenging conditions. The uniqueness of this research lies in its inclusive comparison of lane detection methods under varying conditions, evaluated on a single dataset. This not only serves as a valuable reference, but also opens new possibilities and avenues for future advancements in the field.
Date Issued
2024-10-01
Date Acceptance
2024-02-01
Citation
IEEE Transactions on Intelligent Vehicles, 2024, 9 (10), pp.6462-6473
ISSN
2379-8858
Publisher
Institute of Electrical and Electronics Engineers
Start Page
6462
End Page
6473
Journal / Book Title
IEEE Transactions on Intelligent Vehicles
Volume
9
Issue
10
Copyright Statement
Copyright © 2024 IEEE. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Identifier
http://dx.doi.org/10.1109/tiv.2024.3369733
Publication Status
Published
Date Publish Online
2024-02-26