Vision-based Driver Assistance Systems: Survey, Taxonomy and Advances
This work provides a structured overview for researchers and practitioners in intelligent transportation systems, but it is incremental as it synthesizes existing knowledge without new experimental results.
The paper surveys vision-based driver assistance systems, proposing a consistent terminology and taxonomy, and introduces an abstract model to formalize a top-down view for scaling towards autonomous driving.
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.