Behavior prediction at multiple time-scales in inner-city scenarios

We present a flexible and scalable architecture that can learn to predict the future behavior of a vehicle in inner-city traffic. While behavior prediction studies have mainly been focusing on lane change events on highways, we apply our approach to a simple inner-city scenario: approaching a traffic light. Our system employs dynamic information about the current ego-vehicle state as well as static information about the scene, in this case position and state of nearby traffic lights.