Understanding driver-vehicle interactions remains a challenge, particularly in the case of cornering. This is particularly the case for powered two-wheeler vehicle (PTWs) users, perhaps because PTW drivers play a greater role in controlling the stability of their vehicles than do four-wheeled vehicle drivers. This difficulty stems from the variety of practices of this population of road users when entering, controlling their path, and exiting turns. Thus, observing the evolution of rider behavior during a cornering maneuver is an essential step in identifying road environment features that are risk factors for this category of road users. The real data set used for the experiments reported here was collected in the framework of the VIROLO ++ collaborative project to improve the knowledge of the real practices of PTWs drivers, particularly during cornering. The in-depth analysis of these data in order to better understand motorcyclists' behavior can therefore be considered a challenge. For this purpose, a two-step methodology was applied: (1) a data segmentation and feature extraction step in which the multidimensional time series of roll angle and roll velocity data were segmented using a multiple regression hidden logistic process (MRHLP), and (2) a clustering step in which the two detected segments characterizing the curve entry were assigned to different clusters regarding the curve initiation and the control actions set up during the different phases of the turning maneuver based on the hierarchical clustering algorithm. The results obtained show the effectiveness of the proposed methodology.