  As autonomous vehicles (AVs) become increasingly prevalent, their interaction
with human drivers presents a critical challenge. Current AVs lack social
awareness, causing behavior that is often awkward or unsafe. To combat this,
social AVs, which are proactive rather than reactive in their behavior, have
been explored in recent years. With knowledge of robot-human interaction
dynamics, a social AV can influence a human driver to exhibit desired behaviors
by strategically altering its own behaviors. In this paper, we present a novel
framework for achieving human influence. The foundation of our framework lies
in an innovative use of control barrier functions to formulate the desired
objectives of influence as constraints in an optimal control problem. The
computed controls gradually push the system state toward satisfaction of the
objectives, e.g. slowing the human down to some desired speed. We demonstrate
the proposed framework's feasibility in a variety of scenarios related to
car-following and lane changes, including multi-robot and multi-human
configurations. In two case studies, we validate the framework's effectiveness
when applied to the problems of traffic flow optimization and aggressive
behavior mitigation. Given these results, the main contribution of our
framework is its versatility in a wide spectrum of influence objectives and
mixed-autonomy configurations.
