Car-following Behavior Model Learning Using Timed Automata

Yihuan Zhang, Qin Lin, Jun Wang, Sicco Verwer

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

6 Citations (Scopus)
21 Downloads (Pure)

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.
Original languageEnglish
Title of host publicationIFAC-PapersOnLine
EditorsD. Dochain, D. Henrion, D. Peaucelle
PublisherElsevier
Pages2353-2358
Number of pages6
DOIs
Publication statusPublished - Jul 2017
Event20th World Congress of the International Federation of Automatic Control (IFAC), 2017 - Toulouse, France
Duration: 9 Jul 201714 Jul 2017
Conference number: 20
https://www.ifac2017.org

Publication series

NameIFAC-PapersOnLine
PublisherElsevier
Number1
Volume50
ISSN (Electronic)2405-8963

Conference

Conference20th World Congress of the International Federation of Automatic Control (IFAC), 2017
Abbreviated titleIFAC 2017
CountryFrance
CityToulouse
Period9/07/1714/07/17
Internet address

Keywords

  • real-time automata learning
  • state sequence clustering
  • car-following behavior
  • piece-wise fitting

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