Intuitive visualization of vehicle distance, velocity and risk potential in rear-view camera applications

Abstract

Many serious collisions on highways happen while changing lanes. One of the main causes for these accidents is the driver's incorrect assessment of the current rear traffic situation. To support the driver, we propose a framework to intuitively visualize distance, speed and risk potential of approaching vehicles in a rear-view camera application. The proposed visualization techniques are based on color coding, artificial motion blur and depth-of-field rendering, which are motivated by sensory effects of the human eye and interpreted intuitively by the human visual system. The impact on the human assessment of the moving speed of an object rendered with artificial motion enhancement is evaluated in a user study. The required distance and motion estimation of the vehicles are extracted out of monocular video images, by combining lane recognition, vehicle detection and segmentation machine vision algorithms.