Learning Risky Driver Behaviours from Multi-Channel Data Streams Using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Xie:2013:AI,
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author = "Feng Xie and Andy Song and Flora Salim and
Athman Bouguettaya and Timos Sellis and Doug Bradbrook",
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title = "Learning Risky Driver Behaviours from Multi-Channel
Data Streams Using Genetic Programming",
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booktitle = "Proceedings of the 26th Australasian Joint Conference
on Artificial Intelligence (AI2013)",
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year = "2013",
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editor = "Stephen Cranefield and Abhaya Nayak",
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volume = "8272",
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series = "LNAI",
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pages = "202--213",
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address = "Dunedin, New Zealand",
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month = "1-6 " # dec,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, smartphone",
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isbn13 = "978-3-319-03679-3",
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URL = "http://dx.doi.org/10.1007/978-3-319-03680-9_22",
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DOI = "doi:10.1007/978-3-319-03680-9_22",
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size = "12 pages",
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abstract = "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 recognise such behaviours from smart phone
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.",
- }
Genetic Programming entries for
Feng Xie
Andy Song
Flora Salim
Athman Bouguettaya
Timos Sellis
Doug Bradbrook
Citations