Detecting unsafe driving patterns using discriminative learning

We propose a discriminative learning approach for fusing multichannel sequential data with application to detect unsafe driving patterns from multi-channel driving recording data. The fusion is performed using a discriminatively trained graphical model - conditional random field (CRF). The proposed approach offers several advantage over existing information fusing approaches. First, it derives its classification power by directly modelling and maximizing the conditional probability. Second, it represents the variable dependency in an undirected graph, which is very efficient in inference. Third, it does not require to label all the training data and utilizes both labelled and unlabelled data efficiently by semi-supervised learning algorithms. The proposed approach is evaluated on driving recording data collected from driving simulator - STISIM. Experiments show it outperforms the simple discriminative classifier (SVM) and generative model (HMM).