Learning Driving Behavior by Timed Syntactic Pattern Recognition

Learning Driving Behavior by Timed Syntactic Pattern Recognition

Verwer, S.E.
De Weerdt, M.M.
Witteveen, C.

Electrical Engineering, Mathematics and Computer Science

Software Computer Technology

2011-07-16

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.

http://resolver.tudelft.nl/uuid:64c3560c-8d59-48d4-807e-d4d03e6fd1ad

https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-257

AAAI Press/International Joint Conferences on Artificial Intelligence

978-1-57735-516-8

Proceedings of the 22nd International Joint Conference on Artificial Intelligence, Barcelona, Spain, 16-22 July 2011

Institutional Repository

conference paper

© 2011 International Joint Conferences on Artificial Intelligence