Classification of Highway Lane Change Behavior to Detect Dangerous Cut-in Maneuvers

Recognizing dangerous driver behavior is an essential part of predicting accurate vehicle trajectories in vehicle active safety systems. This paper proposes a lane change behavior classification approach to detect dangerous cut-in behaviors on highways. First, a probabilistic lane change behavior classifier is proposed based on Hidden Markov Models (HMMs). Then, time series data of lane changing vehicles from both normal and dangerous driving data sets are analyzed and compared to extract decisive features that are more likely to appear in dangerous lane change processes. A feature detection module is proposed specifically considering decisive features correlated to dangerous lane change. Furthermore, the feature detection module is integrated into the HMM classifier to enhance classification ability. The proposed classifier is verified with a separate test data set, and shows satisfactory results in reducing false negative rate of misclassification.