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.