Car-following Behavior Model Learning Using Timed Automata

Learning driving behavior is fundamental for autonomous vehicles to “understand” traffic situations. This paper proposes a novel method for learning a behavioral model of car-following 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.

[1]  Montasir M. Abbas,et al.  Segmentation and Clustering of Car-Following Behavior: Recognition of Driving Patterns , 2015, IEEE Transactions on Intelligent Transportation Systems.

[2]  Helbing,et al.  Congested traffic states in empirical observations and microscopic simulations , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[3]  Colin de la Higuera,et al.  A bibliographical study of grammatical inference , 2005, Pattern Recognit..

[4]  James R. Cordy,et al.  A survey of grammatical inference in software engineering , 2014, Sci. Comput. Program..

[5]  Cees Witteveen,et al.  Identifying an automaton model for timed data , 2006 .

[6]  E. Mark Gold,et al.  Complexity of Automaton Identification from Given Data , 1978, Inf. Control..

[7]  L. K. Hansen,et al.  On Clustering fMRI Time Series , 1999, NeuroImage.

[8]  Marcello Montanino,et al.  Trajectory data reconstruction and simulation-based validation against macroscopic traffic patterns , 2015 .

[9]  E. Mark Gold,et al.  Language Identification in the Limit , 1967, Inf. Control..

[10]  Cees Witteveen,et al.  A Likelihood-Ratio Test for Identifying Probabilistic Deterministic Real-Time Automata from Positive Data , 2010, ICGI.

[11]  D. Gazis,et al.  Nonlinear Follow-the-Leader Models of Traffic Flow , 1961 .

[12]  Sicco Verwer.  Efficient Identification of Timed Automata: Theory and practice , 2010 .

[13]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[14]  Cees Witteveen,et al.  Learning Driving Behavior by Timed Syntactic Pattern Recognition , 2011, IJCAI.

[15]  Yasubumi Sakakibara,et al.  Recent Advances of Grammatical Inference , 1997, Theor. Comput. Sci..

[16]  L. A. Pipes.  An Operational Analysis of Traffic Dynamics , 1953 .