A two-step segmentation algorithm for behavioral clustering of naturalistic driving styles

This research effort aims to investigate the hypothesis that drivers apply different driving styles in their daily driving tasks. A two-step algorithm is used for segmentation and clustering. First, a car-following period is broken into different duration segments that account for their temporal distribution. Second, the segments produced by the previous step are clustered based on similarity. Variations of k-means clustering and optimization techniques are used in this process. The segments centroids, used for clustering, are 8-dimensional and are produced by taking the average of the data points in each segment based on longitudinal acceleration, lateral acceleration, gyro (yaw rate), vehicle speed, lane offset, gamma (yaw angle), range, and range rate. The results of this methodology are continuous segments of car-following behavior as well as clusters of segments that show similar data and thus similar behaviors. The sample used in this paper included three different truck drivers that are representative of a high-risk driver, a medium-risk driver, and a low-risk driver. . In summary, the results revealed behavior that changed within a car-following period, between car-following periods, and between drivers. Each driver showed a unique distribution of behavior, but some of the behaviors existed in more than one driver but at different frequencies.