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Car-following Behavior Model Learning Using Timed Automata

Conference Paper (2017)
Author(s)

Yihuan Zhang (Tongji University)

Qin Lin (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Jun Wang (Tongji University)

Sicco Verwer (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Research Group
Cyber Security
DOI related publication
https://doi.org/10.1016/j.ifacol.2017.08.423
URL related publication
https://www.researchgate.net/publication/316923025_Learning_Car-following_Behaviors_Using_Timed_Automata
More Info
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Publication Year
2017
Language
English
Research Group
Cyber Security
Pages (from-to)
2353-2358
Publisher
Elsevier
Event
20th World Congress of the International Federation of Automatic Control (IFAC), 2017 (2017-07-09 - 2017-07-14), Toulouse, France
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316
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Abstract

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.