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

  • Feng Xie
  • Andy Song
  • Flora Salim
  • Athman Bouguettaya
  • Timos Sellis
  • Doug Bradbrook
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8272)

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 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.

Keywords

True Negative Road Safety Driver Behaviour Road Test Drunk Driving 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Feng Xie
    • 1
  • Andy Song
    • 1
  • Flora Salim
    • 1
  • Athman Bouguettaya
    • 1
  • Timos Sellis
    • 1
  • Doug Bradbrook
    • 1
  1. 1.RMIT University, Mornington Peninsula Shire CouncilAustralia

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