Traffic Signal Detection and Recognition Algorithms for Autonomous Vehicles: A Brief Review
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 150, Issue 10
Abstract
In this paper, we present a brief review of the most prevalent computer vision–based traffic signal recognition studies in the literature. Based on the adopted computer vision approaches, we classify the traffic signal recognition studies into three categories: model-based, classical machine learning–based, and deep learning–based methods. Additionally, we include an extensive analysis of the traffic signal data sets used for training and evaluating traffic signal recognition deep learning models. This paper provides researchers and practitioners with insight into the research trends in traffic signal recognition used in vehicle perception, emphasizing various adopted methodologies and their detailed performance parameters.
Practical Applications
Traffic signal recognition is a crucial component of autonomous driving and involves detecting and recognizing different states of traffic signals. Over the years, researchers have explored a wide range of digital image processing and machine learning methods for traffic signal recognition applications. This paper provides a brief literature review of the most prominent traffic signal recognition studies. Innovations in this field are crucial to revolutionizing how autonomous vehicles interact with the existing traffic signal infrastructure and the surrounding traffic, leading to improved safety and better traffic management. Some of the significant challenges in this field derive from the requirements of addressing different lighting conditions, weather, and occlusions, which makes the ongoing research indispensable.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon request.
Acknowledgments
This project is funded by the Transportation Consortium of South-Central States, Tran-SET (Grant 69A3551747106) through Project No. 22ITSLSU41. Portions of this research were conducted with high-performance computing (HPC) resources provided by Louisiana State University (http://www.hpc.lsu.edu).
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Journal of Transportation Engineering, Part A: Systems
Volume 150 • Issue 10 • October 2024
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© 2024 American Society of Civil Engineers.
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Published online: Aug 1, 2024
Published in print: Oct 1, 2024
Discussion open until: Jan 1, 2025
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