  Driver identification has emerged as a vital research field, where both
practitioners and researchers investigate the potential of driver
identification to enable a personalized driving experience. Within recent
years, a selection of studies have reported that individuals could be perfectly
identified based on their driving behavior under controlled conditions.
However, research investigating the potential of driver identification under
naturalistic conditions claim accuracies only marginally higher than random
guess. The paper at hand provides a comprehensive summary of the recent work,
highlighting the main discrepancies in the design of the machine learning
approaches, primarily the window length parameter that was considered. Key
findings further indicate that the longitudinal vehicle control information is
particularly useful for driver identification, leaving the research gap on the
extent to which the lateral vehicle control can be used for reliable
identification. Building upon existing work, we provide a novel approach for
the design of the window length parameter that provides evidence that reliable
driver identification can be achieved with data limited to the steering wheel
only. The results and insights in this paper are based on data collected from
the largest naturalistic driving study conducted in this field. Overall, a
neural network based on GRUs was found to provide better identification
performance than traditional methods, increasing the prediction accuracy from
under 15\% to over 65\% for 15 drivers. When leveraging the full field study
dataset, comprising 72 drivers, the accuracy of identification prediction of
the approach improved a random guess approach by a factor of 25.
