Vision-Based Driver Assistance Systems: Survey, Taxonomy and Advances
By Jonathan Horgan, Ciarán Hughes, John McDonald Jr., Senthil Kumar Yogamani. 2015
64 citations recorded by the source corpus.
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.
Subfields: Automotive Engineering, Computer Vision and Pattern Recognition, Robotics & Control
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Automotive Engineering
- Ciarán Hughes
- Patrick Pérez
- Karl Amende
- Stefan Milz
- Martin Simon
- Senthil Kumar Yogamani
- Jonathan Horgan
- John McDonald Jr.
In Computer Vision and Pattern Recognition
- Senthil Kumar Yogamani
- Ganesh Sistu
- Stefan Milz
- Sumanth Chennupati
- Karl Amende
- Michal Uřičář
- Martin Simon
- Hazem Rashed
In Robotics & Control
Citations in the corpus
- WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving (2019, background): “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].”
- Computer vision in automated parking systems: Design, implementation and challenges (2017, background): “Figure 1 shows the var-Preprint ious field of views of common ADAS applications [2], some of which is needed for parking systems.”
- SoilingNet: Soiling Detection on Automotive Surround-View Cameras (2019, background): “[6] provides an overview of various visual perception tasks prior to deep learning era and Sistu et al.”