  Effective driving style analysis is critical to developing human-centered
intelligent driving systems that consider drivers' preferences. However, the
approaches and conclusions of most related studies are diverse and inconsistent
because no unified datasets tagged with driving styles exist as a reliable
benchmark. The absence of explicit driving style labels makes verifying
different approaches and algorithms difficult. This paper provides a new
benchmark by constructing a natural dataset of Driving Style (100-DrivingStyle)
tagged with the subjective evaluation of 100 drivers' driving styles. In this
dataset, the subjective quantification of each driver's driving style is from
themselves and an expert according to the Likert-scale questionnaire. The
testing routes are selected to cover various driving scenarios, including
highways, urban, highway ramps, and signalized traffic. The collected driving
data consists of lateral and longitudinal manipulation information, including
steering angle, steering speed, lateral acceleration, throttle position,
throttle rate, brake pressure, etc. This dataset is the first to provide
detailed manipulation data with driving-style tags, and we demonstrate its
benchmark function using six classifiers. The 100-DrivingStyle dataset is
available via https://github.com/chaopengzhang/100-DrivingStyle-Dataset
