Feature abstraction for driver behaviour detection with stacked sparse auto-encoders

Zehra Camlica, Allaa Hilal, Dana Kulic

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

7 Citations (Scopus)


Driver behaviour has a significant influence on vehicle accidents. Measuring and providing feedback on driver behaviour can provide significant benefits for understanding and improving road safety. In order to detect driver actions and driving characteristics from the broadest population of drivers, mobile phones can be used to collect low cost information and provide easy accessibility, using sensors available on the mobile phone such as the GPS and IMU. Such information is collected as a time series dataset, which generally has high dimensional variables. Dealing with this high dimensional data is a crucial problem for statistical analysis. Feature abstraction techniques can reduce the dimensionality by extracting salient features from the dataset. This paper proposes a feature abstraction method using stacked sparse autoencoders in order to reduce driver dataset variables. The utility of the derived features is demonstrated on a driver action classification task.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Systems, Man, and Cybernetics - Conference Proceedings
EditorsShun-Feng Su
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781509018970, 9781509018192
ISBN (Print)9781509018987
Publication statusPublished - 2016
Externally publishedYes
EventIEEE International Conference on Systems, Man and Cybernetics 2016 - Budapest, Hungary
Duration: 9 Oct 201612 Oct 2016
https://ieeexplore.ieee.org/xpl/conhome/7830913/proceeding (Proceedings)


ConferenceIEEE International Conference on Systems, Man and Cybernetics 2016
Abbreviated titleSMC 2016
Internet address

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