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Car-following is the most general behavior in highway driving. It is crucial to recognize the cut-in intention of vehicles from an adjacent lane for safe and cooperative driving. In this paper, a method of behavior estimation is proposed to recognize and predict the lane change intentions based on the contextual traffic information. A model predictive controller is designed to optimize the acceleration sequences by incorporating the lane-change intentions of other vehicles. The public dataset of Next Generation Simulation are labeled and then published as a benchmarking platform for the research community. Experimental results demonstrate that the proposed method can accurately estimate vehicle behavior and therefore outperform the traditional car-following control.Index Terms-cooperative car-following, driving behavior estimation, lane change prediction, model predictive control.

This paper proposes a novel hybrid model for learning discrete and continuous dynamics of car-following behaviors. Multiple modes representing driving patterns are identified by partitioning the model into groups of states. The model is visualizable and interpretable for car-following behavior recognition, traffic simulation, and human-like cruise control. The experimental results using the next generation simulation datasets demonstrate its superior fitting accuracy over conventional models. Index Terms-Hybrid automaton, car-following behavior, simulation and control.Qin Lin is currently pursuing the Ph.D. degree with the Department of Intelligent Systems, Delft University of Technology. His research interests include machine learning, time series data mining, and syntactic pattern recognition.

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Learning driving behavior is fundamental for autonomous vehicles to "understand" traffic situations. This paper proposes a novel method for learning a behavioral model of carfollowing using automata learning algorithms. The model is interpretable for car-following behavior analysis. Frequent common state sequences are extracted from the model and clustered as driving patterns. The Next Generation SIMulation dataset on the I-80 highway is used for learning and evaluating. The experimental results demonstrate high accuracy of car-following model fitting.