Learning Driving Behavior by Timed Syntactic Pattern Recognition
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 1529-1533.
Abstract
We advocate the use of an explicit time representation in syntactic pattern recognition because it can result in more succinct models and easier learning problems. We apply this approach to the real-world problem of learning models for the driving behavior of truck drivers. We discretize the values of onboard sensors into simple events. Instead of the common syntactic pattern recognition approach of sampling the signal values at a fixed rate, we model the time constraints using timed models. We learn these models using the RTI+ algorithm from grammatical inference, and show how to use computational mechanics and a form of semi-supervised classification to construct a real-time automaton classifier for driving behavior. Promising results are shown using this new approach.
@inproceedings{ijcai2011p257,
title = {Learning Driving Behavior by Timed Syntactic Pattern Recognition},
author = {Verwer, Sicco and de Weerdt, Mathijs and Witteveen, Cees},
booktitle = {Proceedings of the Twenty-Second International Joint Conference on
Artificial Intelligence, {IJCAI-11}},
publisher = {AAAI Press/International Joint Conferences on Artificial Intelligence},
editor = {Toby Walsh},
pages = {1529--1533},
year = {2011},
month = {7},
doi = {10.5591/978-1-57735-516-8/IJCAI11-257},
url = {https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-257},
}
TY - CPAPER TI - Learning Driving Behavior by Timed Syntactic Pattern Recognition AU - Verwer, Sicco AU - de Weerdt, Mathijs AU - Witteveen, Cees T2 - Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11 PB - AAAI Press/International Joint Conferences on Artificial Intelligence PY - 2011 SP - 1529 EP - 1533 DO - 10.5591/978-1-57735-516-8/IJCAI11-257 UR - https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-257 ER -Copied
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