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Driver identification has emerged as a vital research field, where bothpractitioners and researchers investigate the potential of driveridentification to enable a personalized driving experience. Within recentyears, a selection of studies have reported that individuals could be perfectlyidentified based on their driving behavior under controlled conditions.However, research investigating the potential of driver identification undernaturalistic conditions claim accuracies only marginally higher than randomguess. The paper at hand provides a comprehensive summary of the recent work,highlighting the main discrepancies in the design of the machine learningapproaches, primarily the window length parameter that was considered. Keyfindings further indicate that the longitudinal vehicle control information isparticularly useful for driver identification, leaving the research gap on theextent to which the lateral vehicle control can be used for reliableidentification. Building upon existing work, we provide a novel approach forthe design of the window length parameter that provides evidence that reliabledriver identification can be achieved with data limited to the steering wheelonly. The results and insights in this paper are based on data collected fromthe largest naturalistic driving study conducted in this field. Overall, aneural network based on GRUs was found to provide better identificationperformance than traditional methods, increasing the prediction accuracy fromunder 15\% to over 65\% for 15 drivers. When leveraging the full field studydataset, comprising 72 drivers, the accuracy of identification prediction ofthe approach improved a random guess approach by a factor of 25.

Driver Identification via the Steering Wheel