  This paper presents a personalized adaptive cruise control (PACC) design that
can learn driver behavior and adaptively control the semi-autonomous vehicle
(SAV) in the car-following scenario, and investigates its impacts on mixed
traffic. In mixed traffic where the SAV and human-driven vehicles share the
road, the SAV's driver can choose a PACC tuning that better fits the driver's
preferred driving behaviors. The individual driver's preferences are learned
through the inverse reinforcement learning (IRL) approach by recovering a
unique cost function from the driver's demonstrated driving data that best
explains the observed driving style. The proposed PACC design plans the motion
of the SAV by minimizing the learned unique cost function considering the short
preview information of the preceding human-driven vehicle. The results reveal
that the learned driver model can identify and replicate the personalized
driving behaviors accurately and consistently when following the preceding
vehicle in a variety of traffic conditions. Furthermore, we investigated the
impacts of the PACC with different drivers on mixed traffic by considering time
headway, gap distance, and fuel economy assessments. A statistical
investigation shows that the impacts of the PACC on mixed traffic vary among
tested drivers due to their intrinsic driving preferences.
