Abstract: Methods and apparatus, including computer program products, implementing and using techniques for identifying a driver of a vehicle. Measurement values representing the movement of a vehicle are received from one or more sensors measuring features relating to the movement of the vehicle. Instantaneous dimensions for measuring driver identification are defined. For each dimension statistical features within a given time frame are calculated and a feature map is built, including the time frame and the statistical features. A set of driving features is extracted from the feature map. The set of extracted driving features is compared with previously extracted sets of driving features to create a set of similarity metrics between two drivers. A classification model is trained based on the similarity metrics and is used with the similarity metrics to determine whether data pertaining to a new trip segment should be associated with a known or unknown driver.

Abstract: Methods and apparatus, including computer program products, implementing and using techniques for identifying a driver of a vehicle. Measurement values representing the movement of a vehicle are received from one or more sensors measuring features relating to the movement of the vehicle. Instantaneous dimensions for measuring driver identification are defined. For each dimension statistical features within a given time frame are calculated and a feature map is built, including the time frame and the statistical features. A set of driving features is extracted from the feature map. The set of extracted driving features is compared with previously extracted sets of driving features to create a set of similarity metrics between two drivers. A classification model is trained based on the similarity metrics and is used with the similarity metrics to determine whether data pertaining to a new trip segment should be associated with a known or unknown driver.