Driver Information Embedding with Siamese LSTM networks

Recently, the problem of driver classification has received considerable attention in the literature. Most approaches formulate this problem as a classification task, in which the drivers are the classes. The number of classes is thus fixed in the training and test set. By this formulation, a model that is trained to classify two drivers $D_{1}$ and $D_{2}$ can not be used to classify other drivers (e.g, $D_{3}$ and $D_{4}$). In this paper, we formulate the problem of driver identification as a comparison problem, in which a model should learn to extract individual characteristics of drivers and use them as a basis for the comparison. To tackle this problem, we propose an approach using a Siamese network architecture in combination with Long Short-Term Memory (LSTM) for mapping maneuver execution into a lower-dimensional space. The network is trained in such a way that it maps maneuver executions of the same driver into similar vectors in the embedding space and maneuver executions from different drivers to dissimilar vectors in this space. Our approach shows various advantages over the classification-based setting, most notably that it can be used to identify drivers that are not in the training set. Furthermore, the distance between embedding vectors of different drivers can be used as a scalar for measuring the similarity of their driving styles. In addition, since the network only uses a single maneuver pair at a time for producing the prediction, we show that the identification performance theoretically and empirically increases along with the number of seen maneuvers. Finally, the embedding vector can be used as feature to represent the driver or to personalize the assistance systems.