Unsupervised learning of motion patterns of rear surrounding vehicles

Awareness to a vehicle's surrounding is necessary for safe driving. Current surround technologies focus on the detection of obstacles in hard-to-view places but may neglect temporal information. This paper seeks the causes of dangerous situations by examining surround behavior. A general hierarchical learning framework is introduced to automatically learn surround behaviors. By observing motion trajectories during natural driving, models of rear vehicle behaviors are obtained in an unsupervised fashion. The extracted behaviors are shown to correspond to typical driving scenarios, vehicle overtake and surround overtake, demonstrating the effectiveness of the learning framework.