  Personalization of autonomous vehicles (AV) may significantly increase trust,
use, and acceptance. In particular, we hypothesize that the similarity of an
AV's driving style compared to the end-user's driving style will have a major
impact on end-user's willingness to use the AV. To investigate the impact of
driving style on user acceptance, we 1) develop a data-driven approach to
personalize driving style and 2) demonstrate that personalization significantly
impacts attitudes towards AVs. Our approach learns a high-level model that
tunes low-level controllers to ensure safe and personalized control of the AV.
The key to our approach is learning an informative, personalized embedding that
represents a user's driving style. Our framework is capable of calibrating the
level of aggression so as to optimize driving style based upon driver
preference. Across two human subject studies (n = 54), we first demonstrate our
approach mimics the driving styles of end-users and can tune attributes of
style (e.g., aggressiveness). Second, we investigate the factors (e.g., trust,
personality etc.) that impact homophily, i.e. an individual's preference for a
driving style similar to their own. We find that our approach generates driving
styles consistent with end-user styles (p<.001) and participants rate our
approach as more similar to their level of aggressiveness (p=.002). We find
that personality (p<.001), perceived similarity (p<.001), and high-velocity
driving style (p=.0031) significantly modulate the effect of homophily.
