, Associate Professor, UMD-Computer Science
Following a snowplow (or any vehicle) during snowy conditions can be extremely dangerous. The danger comes from the inability of the human visual system to accurately perceive the speed and motion of the lead vehicle, increasing the potential for rear-end collisions. The goal of this project is to create a simulation framework for testing emergency lighting configurations that reduce rear-end collisions with snowplows, based on how the human visual system processes motion under blowing snow conditions. Reaction times for detecting the motion of the snowplow were measured empirically for a variety of color set-ups on a simulated snowplow that slows down while driving on a virtual road with curves and hills. The simulated driving environment utilized a head-mounted, virtual reality display to render an improved snow cloud model behind the snowplow. This driving simulator environment served as the basis for testing the effects of color and lighting alternatives on snowplows. The results of this work will move the researchers closer to determining optimal color and lighting configurations on actual snowplows.