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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Xie:2013:AI,

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