A data-driven behavior generation algorithm in car-following scenarios
Conference Paper, Proceedings of 25th International Symposium on Dynamics of Vehicles on Roads and Tracks (IAVSD '17), pp. 227 - 232, August, 2017
Abstract
The conventional Adaptive Cruise Control system lacks user-friendly design. In this paper, a novel method for learning a generative model from human drivers’ car-following data using an automaton learning algorithms is proposed. By partitioning the model using frequent common state sequences, human driving patterns are extracted and clustered. Then a cluster identification method is used to obtain the current driving pattern and generate a desired acceleration. The experiments validate that the simulated trajectories of the proposed method are more similarly to human drivers than those of conventional PID controller.
BibTeX
@conference{Zhang-2017-122447,author = {Y. Zhang and Q. Lin and J. Wang and S. Verwer and J. Dolan},
title = {A data-driven behavior generation algorithm in car-following scenarios},
booktitle = {Proceedings of 25th International Symposium on Dynamics of Vehicles on Roads and Tracks (IAVSD '17)},
year = {2017},
month = {August},
pages = {227 - 232},
}
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