Off-road terrain classification

Abstract

Road traffic accidents place a burden on the global economy. This impact is reduced by the development of safer vehicles. Advanced Driver Assist Systems (ADAS) aim to reduce the frequency and severity of accidents. ADASs are designed to operate in well-defined environments, such as first world urban areas. However, 93% of fatal accidents occur in developing countries; areas often without properly maintained roads. ADAS regularly fail to perform as intended in these challenging environments. Terrain classification may improve the performance of ADAS. A lot of research has been conducted on on-road terrain classification, but few studies focus on off-road terrain classification. This study classifies several off-road terrains, based on road roughness using the ISO8608:2016 standard, using a convolutional neural network (CNN). A database of images over different terrains with known road roughness was created using forward and downward facing cameras. Two different classification models were built: one is brand new and the other made use of transfer learning on pretrained model. Terrain data was captured on several on-road and off-road tracks. Results indicate that off-road terrain classification with cameras can be done with high accuracy before a vehicle drives over a specific part of a road.