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

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.

[1]  Tomohiro Yamamura,et al.  A Driver Behavior Recognition Method Based on a Driver Model Framework , 2000 .

[2]  Marco Aiello,et al.  A Machine Learning Approach for Identifying and Classifying Faults in Wireless Sensor Network , 2012, 2012 IEEE 15th International Conference on Computational Science and Engineering.

[3]  Nadeem Akhtar,et al.  Mobile Application for Safe Driving , 2014, 2014 Fourth International Conference on Communication Systems and Network Technologies.

[4]  Dana Kulic,et al.  An evaluation of classifier-specific filter measure performance for feature selection , 2015, Pattern Recognit..

[5]  Pascal Vincent,et al.  Unsupervised Feature Learning and Deep Learning: A Review and New Perspectives , 2012, ArXiv.

[6]  Asier Perallos,et al.  Vehicle Control in ADAS Applications , 2015 .

[7]  E. Geller,et al.  Self-Management to Increase Safe Driving Among Short-Haul Truck Drivers , 2005 .

[8]  Ian T. Jolliffe,et al.  Principal Component Analysis , 2002, International Encyclopedia of Statistical Science.

[9]  Fatemeh Afghah,et al.  Driver behavior modeling near intersections using support vector machines based on statistical feature extraction , 2015, 2015 IEEE Intelligent Vehicles Symposium (IV).

[10]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[11]  Yoshihiko Nakamura,et al.  Recognition of human driving behaviors based on stochastic symbolization of time series signal , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[12]  Tadahiro Taniguchi,et al.  Visualization of driving behavior using deep sparse autoencoder , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[13]  R. Fuller.  Towards a general theory of driver behaviour. , 2005, Accident; analysis and prevention.

[14]  Li Liu,et al.  Driver behavior analysis based on Bayesian network and multiple classifiers , 2010, 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems.

[15]  Dana Kulic,et al.  Manoeuvre segmentation using smartphone sensors , 2016, 2016 IEEE Intelligent Vehicles Symposium (IV).

[16]  Changyin Sun,et al.  Intelligent Science and Intelligent Data Engineering , 2011, Lecture Notes in Computer Science.

[17]  Mohan M. Trivedi,et al.  Driving style recognition using a smartphone as a sensor platform , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[18]  Paul Smolensky,et al.  Information processing in dynamical systems: foundations of harmony theory , 1986 .