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Learning risky driver behaviours from multi-channel data streams using genetic programming

journal contribution
posted on 2024-11-01, 15:25 authored by Feng Xie, Andy SongAndy Song, Flora SalimFlora Salim, Athman Bouguettaya, Timoleon Sellis, Doug Bradbrook
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.

History

Journal

Lecture Notes in Computer Science

Volume

8272

Start page

202

End page

213

Total pages

12

Publisher

Springer

Place published

Germany

Language

English

Copyright

© Springer International Publishing Switzerland 2013

Former Identifier

2006043402

Esploro creation date

2020-06-22

Fedora creation date

2014-01-20

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