Predicting Unexpected Maneuver While Approaching Intersection

Unfamiliar urban intersections pose high demand on drivers. They are not only engaged in correctly assessing large amount of visual stimuli, including multiple diverse moving objects (e.g. other vehicles, pedestrians, cyclists) but also actively processing instructions provided by navigation system, either in-car or on other devices such as smart-phones. In such a highly dynamic and engaging situation, drivers are prone to make a mistake. In this paper, we look into the intersection behavior of the driver to predict an unexpected maneuver that would cause deviation from the planned path such as missing an upcoming turn or make a last minute aggressive maneuver. We conduct an on-road test of naturally following planned route as suggested by the navigation system. Our ultimate goal is to develop a Advanced Driver Assistance System (ADAS) that can predict unexpected maneuver and help driver in timely manner to correct those mistakes e.g. by providing detail navigation instructions for the driver to better orient himself or herself in a challenging situation. We propose an unexpected maneuver detection framework that can utilize vehicle, map as well as driver information to predict ahead in time. We further illustrate the benefit of utilizing driver information for early prediction.