Superpixel based semantic segmentation for assistance in varying terrain driving conditions

Abstract

Vehicle drivability and maneuverability can be improved by increasing the environment awareness via sensory inputs. In particular, off-road capable vehicles possess subsystems which are configurable to the driving conditions. In this work, a vision solution is explored as a precursor to autonomous toggling between different operating modes. The emphasis is on selecting an appropriate response to transitions from one terrain type to another. Given a forward facing camera, images are partitioned into pixel subsets known as superpixels in order to be classified. The quality of this semantic segmentation is considered for classes such as {grass, tree, sky, tarmac, dirt, gravel, shrubs}. Colour and texture are combined together to form visual cues and address this image recognition problem with good segmentation results. © Springer International Publishing Switzerland 2015.