Jonathan Horgan

Vision-Based Driver Assistance Systems: Survey, Taxonomy and Advances

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.

Optical Flow-Based Moving-Static Separation in Driving Assistance Systems

This paper introduces a novel approach for a moving static separation, which can be efficiently used for outlier removal within optical flow-based 3D reconstruction or moving object detection even in the case of a moving observer. The contributions of the proposed approach include an efficient implementation, high accuracy and embedded friendliness. These characteristics make the approach very attractive for automotive safety applications based on camera systems. The paper also highlights some possible applications where the use of the moving static separation can play a key role on improving the performance.