, Associate Professor, UMD-Computer Science
Following a snowplow during a snowfall can be extremely dangerous. This danger comes from the human visual system's inability to accurately perceive the speed and motion of the snowplow, often resulting in rear-end collisions. This research project creates a simulation framework for testing emergency lighting configurations that reduce rear-end collisions with snowplows. Reaction times for detecting the motion of the snowplow are 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. Current efforts have implemented a blowing snow model that will eventually be integrated into a real-time driving simulation environment. Concurrently, a simulated driving environment has been developed that will serve as the basis for testing the effects of color and lighting alternatives on snowplows. In initial pilot experiments, the simulated driving environment has been effective at testing subject reaction times for following a snowplow through high luminance contrast (normal daylight driving) and low luminance contrast (daylight fog) conditions. The results of this work will move the researchers closer to determining optimal color and lighting configurations on actual snowplows.