Road-Geometry-Based Risk Estimation Model for Horizontal Curves

Rural roads present potential risks for drivers. One of them is horizontal curve, which poses higher risk than freeway. This is the major theme for the presented work here aiming to develop a model that predicts risk of curved roads. Major road geometry components associated with curve structure are road slope type being uphill or downhill, road curvature, and curve direction along with vehicle speed as being a critical factor. In this study, cameras mounted in rear and front ends of a vehicle that capture road images are utilized to detect the components emerging risk. This two-view approach is exploited to obtain vehicle speed and bend slope type, whereas curve direction and road curvature are determined by single-view front camera. The proposed approach is leveraged by geometrical derivations using salient visual clues such as vanishing points and road boundary. Additionally, velocity is estimated by reverse-view technique, that is, plane of front view at instance t and the plane of rear view in t + 1. Subsequently, overall potential hazard is predicted by assigning weights for each risk components via developed risk estimation model. The proposed model would be an integral part of an advanced driver assistant system by alerting driver about the prominent risk of horizontal curve ahead of time.