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Car-following Behavior Model Learning Using Timed Automata. / Zhang, Yihuan; Lin, Qin; Wang, Jun; Verwer, Sicco.

IFAC-PapersOnLine. ed. / D. Dochain; D. Henrion; D. Peaucelle. Elsevier, 2017. p. 2353-2358 (IFAC-PapersOnLine; Vol. 50, No. 1).

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Zhang, Y, Lin, Q, Wang, J & Verwer, S 2017, Car-following Behavior Model Learning Using Timed Automata. in D Dochain, D Henrion & D Peaucelle (eds), IFAC-PapersOnLine. IFAC-PapersOnLine, no. 1, vol. 50, Elsevier, pp. 2353-2358, 20th World Congress of the International Federation of Automatic Control (IFAC), 2017, Toulouse, France, 9/07/17. https://doi.org/10.1016/j.ifacol.2017.08.423

APA

Zhang, Y., Lin, Q., Wang, J., & Verwer, S. (2017). Car-following Behavior Model Learning Using Timed Automata. In D. Dochain, D. Henrion, & D. Peaucelle (Eds.), IFAC-PapersOnLine (pp. 2353-2358). (IFAC-PapersOnLine; Vol. 50, No. 1). Elsevier. https://doi.org/10.1016/j.ifacol.2017.08.423

Vancouver

Zhang Y, Lin Q, Wang J, Verwer S. Car-following Behavior Model Learning Using Timed Automata. In Dochain D, Henrion D, Peaucelle D, editors, IFAC-PapersOnLine. Elsevier. 2017. p. 2353-2358. (IFAC-PapersOnLine; 1). https://doi.org/10.1016/j.ifacol.2017.08.423

Author

Zhang, Yihuan ; Lin, Qin ; Wang, Jun ; Verwer, Sicco. / Car-following Behavior Model Learning Using Timed Automata. IFAC-PapersOnLine. editor / D. Dochain ; D. Henrion ; D. Peaucelle. Elsevier, 2017. pp. 2353-2358 (IFAC-PapersOnLine; 1).

BibTeX

@inproceedings{886516fe764c4d158968406ad9ee2eb1,
title = "Car-following Behavior Model Learning Using Timed Automata",
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.",
keywords = "real-time automata learning, state sequence clustering, car-following behavior, piece-wise fitting",
author = "Yihuan Zhang and Qin Lin and Jun Wang and Sicco Verwer",
year = "2017",
month = "7",
doi = "10.1016/j.ifacol.2017.08.423",
language = "English",
series = "IFAC-PapersOnLine",
publisher = "Elsevier",
number = "1",
pages = "2353--2358",
editor = "D. Dochain and D. Henrion and D. Peaucelle",
booktitle = "IFAC-PapersOnLine",
address = "Netherlands",

}

RIS

TY - GEN

T1 - Car-following Behavior Model Learning Using Timed Automata

AU - Zhang, Yihuan

AU - Lin, Qin

AU - Wang, Jun

AU - Verwer, Sicco

PY - 2017/7

Y1 - 2017/7

N2 - 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.

AB - 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.

KW - real-time automata learning

KW - state sequence clustering

KW - car-following behavior

KW - piece-wise fitting

UR - http://resolver.tudelft.nl/uuid:886516fe-764c-4d15-8968-406ad9ee2eb1

U2 - 10.1016/j.ifacol.2017.08.423

DO - 10.1016/j.ifacol.2017.08.423

M3 - Conference contribution

T3 - IFAC-PapersOnLine

SP - 2353

EP - 2358

BT - IFAC-PapersOnLine

A2 - Dochain, D.

A2 - Henrion, D.

A2 - Peaucelle, D.

PB - Elsevier

ER -

ID: 28357265