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 language | English |
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Title of host publication | IFAC-PapersOnLine |
Editors | D. Dochain, D. Henrion, D. Peaucelle |
Publisher | Elsevier |
Pages | 2353-2358 |
Number of pages | 6 |
DOIs | |
Publication status | Published - Jul 2017 |
Event | 20th World Congress of the International Federation of Automatic Control (IFAC), 2017 - Toulouse, France Duration: 9 Jul 2017 → 14 Jul 2017 Conference number: 20 https://www.ifac2017.org |
Publication series
Name | IFAC-PapersOnLine |
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Publisher | Elsevier |
Number | 1 |
Volume | 50 |
ISSN (Electronic) | 2405-8963 |
Conference
Conference | 20th World Congress of the International Federation of Automatic Control (IFAC), 2017 |
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Abbreviated title | IFAC 2017 |
Country/Territory | France |
City | Toulouse |
Period | 9/07/17 → 14/07/17 |
Internet address |
Keywords
- real-time automata learning
- state sequence clustering
- car-following behavior
- piece-wise fitting