Vision-Based Driver Assistance Systems: Survey, Taxonomy and Advances
Open accessAbstract
Vision-based driver assistance systems is one of the rapidly growing research areas of ITS, due to various factors such as the increased level of safety requirements in automotive, computational power in embedded systems, and desire to get closer to autonomous driving. It is a cross disciplinary area encompassing specialised fields like computer vision, machine learning, robotic navigation, embedded systems, automotive electronics and safety critical software. In this paper, we survey the list of vision based advanced driver assistance systems with a consistent terminology and propose a taxonomy. We also propose an abstract model in an attempt to formalize a top-down view of application development to scale towards autonomous driving system.
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Soiling Detection with ResNet10 achieves a Category (%) of 84.5 on the WoodScape dataset.
“| Soiling Detection | ResNet10 [20] | Category (%) | 84.5 |”
From WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving (2019) · cites this paper as [20]
The paper’s discussion of classification methods (III. Vision Algorithm Building Blocks, p. 4) and its table of established applications (p. 5) do not report a soiling-detection result. Searches for WoodScape, ResNet10, and 84.5 found no passage supporting the attributed dataset, model, or score. (LLM, conf=0.76)
Experimental — accuracy not yet measuredVisual Odometry with ResNet50 achieves a Rotation (<0.1°) score of 71 on the WoodScape dataset.
“| Visual Odometry | ResNet50 [20] | Translation (<5mm) Rotation (<0.1°) | 51 71 |”
From WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving (2019) · cites this paper as [20]
The Motion Estimation section (p. 3) describes tracking and matching image pixels, but reports no visual-odometry benchmark score. Searches for WoodScape, ResNet50, and the stated rotation and translation thresholds found no passage supporting the score of 71. (LLM, conf=0.76)
Experimental — accuracy not yet measuredVisual Odometry with ResNet50 achieves a Translation (<5mm) score of 51 on the WoodScape dataset.
“| Visual Odometry | ResNet50 [20] | Translation (<5mm) Rotation (<0.1°) | 51 71 |”
From WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving (2019) · cites this paper as [20]
The discussion of odometry in 'Towards a generic scalable algorithm framework' (p. 6) concerns its use in segmenting moving objects, not a performance evaluation. Searches for WoodScape, ResNet50, and the claimed translation threshold and score found no passage in our copy reporting that result. (LLM, conf=0.77)
Experimental — accuracy not yet measured
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Sorted by citations
WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving
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“Large FOV cameras are necessary for various computer vision application domains, including video surveillance [28] and augmented reality [46], and have been of particular interest in autonomous driving [23].”
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Self-citationSoilingNet: Soiling Detection on Automotive Surround-View Cameras
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Self-citation- Retracted
- Authors
- Horgan, J., Hughes, C., McDonald, J., & Yogamani, S.
- Affiliations
- Valeo (Ireland) (IE)
- Venue
- 2015 IEEE 18th International Conference on Intelligent Transportation Systems
- Publisher
- IEEE
- Published
- 2015-09
- Pages
- 2032-2039
- OpenAlex
- W1904301353
- Cited by
- 72
- References
- 29
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 4 · 2023: 7 · 2022: 8 · 2021: 9 · 2020: 7 · 2019: 15 · 2018: 3 · 2017: 5 · 2016: 4
- FWCI
- 5.17
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE International Conference on Intelligent Transportation Systems (ITSC) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- driver assistance systems, taxonomy (general classification scheme), autonomous driving, safety-critical software, embedded systems, robot navigation, intelligent transportation systems
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