Learning Risky Driver Behaviours from Multi-Channel Data Streams Using Genetic Programming

Publication Year: 2013 Publication Type : ConferenceProceeding


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.


    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}


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