Visio-spatial road boundary detection for unmarked urban and rural roads

Abstract

The robust detection of road boundaries is a prerequisite for Advanced Driver Assistant Systems (ADAS), such as Lane Departure Warning and Lane Keeping Assistant Systems. State-of-the-art ADAS rely on lane markings to draw inference about the extent of lanes or the road area. However, on many rural or urban roads markings are worn out or simply not existing. Therefore, this publication proposes a system for vision-based road boundary detection without requiring road markings at the outer side of the road. The fundamental approach is a boundary vicinity classification based on SPatial RAY (SPRAY) features which combines visual and spatial context information going beyond classical image patch analysis. More specifically, the inner road boundary vicinity (IBV), the outer road boundary vicinity (OBV), and the remaining part of the road area (RA) are detected. Because these classes occur in a defined sequence, i.e., a road boundary exhibits a transition from RA to IBV to OBV, this approach extracts the horizontal boundary transition pattern to make inference about possible locations of road boundaries and their direction (left or right road side). The implemented system was evaluated on unmarked urban and rural roads. The results show that the system effectively detects road boundaries such as curbstones and soft shoulders under challenging conditions.