Passive vision road obstacle detection: a literature mapping

Abstract

ABSTRACT Obstacle detection is one of the main tasks in intelligent vehicle navigation systems. Several research works focused on the use of passive vision (cameras) to accomplish this task have been published. In this paper we present a literature mapping of the state of the art in road obstacle detection using frontal images. This mapping is based upon a systematic literature review that took into consideration papers published between 2007 and 2019. We analyze approaches based upon methods such as image segmentation, stereo vision, optical flow and neural networks and classify them accordingly to their characteristics and detection targets, such as vehicles, pedestrians or obstacles in general. We also inspect if they are performing pavement defects detection, such as potholes, puddles or other types of damage. The detection of pavement problems is important for the reality of in-development countries, which in many cases do not present well-maintained roads and may represent a threat to vehicular navigation. With this mapping we can identify the current state in this research area and also discuss future steps.