Risky driver behaviours such as sudden braking, swerving, and excessive acceleration are a major risk to road safety. In this study, we present a learning method to recognize such behaviours from smartphone sensor input which can be considered as a type of multi-channel time series. Unlike other learning methods, this Genetic Programming (GP) based method does not require pre-processing and manually designed features. Hence domain knowledge and manual coding can be significantly reduced by this approach. This method can achieve accurate real-time recognition of risky driver behaviours on raw input and can outperform classic learning methods operating on features. In addition this GP-based method is general and suitable for detecting multiple types of driver behaviours.
@inproceedings{DBLP:conf/ausai/XieSSBSB13,
author = {Feng Xie and
Andy Song and
Flora Dilys Salim and
Athman Bouguettaya and
Timos K. Sellis and
Doug Bradbrook},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/conf/ausai/XieSSBSB13.bib},
booktitle = {AI 2013: Advances in Artificial Intelligence - 26th Australasian
Joint Conference, Dunedin, New Zealand, December 1-6, 2013. Proceedings},
doi = {10.1007/978-3-319-03680-9_22},
editor = {Stephen Cranefield and
Abhaya C. Nayak},
pages = {202--213},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
timestamp = {Fri, 27 Mar 2020 00:00:00 +0100},
title = {Learning Risky Driver Behaviours from Multi-Channel Data Streams Using
Genetic Programming},
url = {https://doi.org/10.1007/978-3-319-03680-9_22},
volume = {8272},
year = {2013}
}
© 2021 Flora Salim - CRUISE Research Group.