Vehicle Detection Techniques for Collision Avoidance Systems: A Review
Abstract
Over the past decade, vision-based vehicle detection techniques for road safety improvement have gained an increasing amount of attention. Unfortunately, the techniques suffer from robustness due to huge variability in vehicle shape (particularly for motorcycles), cluttered environment, various illumination conditions, and driving behavior. In this paper, we provide a comprehensive survey in a systematic approach about the state-of-the-art on-road vision-based vehicle detection and tracking systems for collision avoidance systems (CASs). This paper is structured based on a vehicle detection processes starting from sensor selection to vehicle detection and tracking. Techniques in each process/step are reviewed and analyzed individually. Two main contributions in this paper are the following: survey on motorcycle detection techniques and the sensor comparison in terms of cost and range parameters. Finally, the survey provides an optimal choice with a low cost and reliable CAS design in vehicle industries.
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Because of low power consumption and low cost, a large number of roadside WSNs may be strategically positioned to help vehicle communication technology.
“Because of its low power consumption and low cost, a large number of roadside WSNs may be strategically positioned to help vehicle communication technology [48].”
From Smart Transportation: An Overview of Technologies and Applications (2023) · cites this paper as [48]
Cites
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Perception, Planning, Control, and Coordination for Autonomous Vehicles
Pendleton, S., Andersen, H., Du, X., Shen, X., Meghjani, M., Eng, Y., Rus, D., & Ang, M. (2017) · Machines · doi:10.3390/machines5010006 · Cited by 657 · see citing works
Smart Transportation: An Overview of Technologies and Applications
Oladimeji, D., Gupta, K., Kose, N. A., Gundogan, K., Ge, L., & Liang, F. (2023) · Sensors · doi:10.3390/s23083880 · Cited by 386 · see citing works
Vehicle detection in severe weather based on pseudo-visual search and HOG–LBP feature fusion
Wang, Z., Zhan, J., Duan, C., Guan, X., & Yang, K. (2022) · Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering · doi:10.1177/09544070211036311 · Cited by 33 · see citing works
- Authors
- Mukhtar, A., Xia, L., & Tang, T. B.
- Affiliations
- Universiti Teknologi Petronas (MY)
- Beijing Institute of Technology (CN)
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2015-10
- Volume
- 16
- Issue
- 5
- Pages
- 2318-2338
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W2088173505
- Cited by
- 427
- References
- 4
- Citations by year
- 2026: 7 · 2025: 24 · 2024: 43 · 2023: 36 · 2022: 45 · 2021: 63 · 2020: 53 · 2019: 60 · 2018: 42 · 2017: 34 · 2016: 18 · 2015: 2
- FWCI
- 33.05
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Intelligent Transportation Systems (publishedVersion)
- Funders
- Ministry of Science, Technology and Innovation (MOSTI), Malaysia (03-02-02-SF0151)
- Ministry of Education, Malaysia (HiCOE grant)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Keywords
- vehicle detection, collision avoidance systems, vision-based detection, vehicle tracking, motorcycle detection, road safety, sensor selection, cluttered environments, illumination variation
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