Extracting interesting vehicle sensor data using multivariate stationarity

Unsupervised modeling of sequentially sampled sensor data typically results in modeling resources getting allocated in proportion to the occurrence of different phenomena in the training data. This is a problem when most of the data is uninteresting but there are rare interesting events. As a consequence of this inbalance, the rare events are either not become well represented in the model or an undesirably large model is needed to satisfy performance measures. We present an approach to resample the data in proportion to the interestingness of the data where interestingness is defined as the multivariate stationarity of a weighted set of important variables. We present a case in modeling vehicle sensor data with the intent of modeling driver actions and traffic situations. We analyzed driving simulator data with this approach and report results where instance selection using the interestingness filtering resulted in models that correspond much better to human classification of different driving situations.