  Connected and autonomous vehicles (CAVs) have great potential to improve road
transportation systems. Most existing strategies for CAVs' longitudinal control
focus on downstream traffic conditions, but neglect the impact of CAVs'
behaviors on upstream traffic flow. In this paper, we introduce a notion of
Leading Cruise Control (LCC), in which the CAV maintains car-following
operations adapting to the states of its preceding vehicles, and also aims to
lead the motion of its following vehicles. Specifically, by controlling the
CAV, LCC aims to attenuate downstream traffic perturbations and smooth upstream
traffic flow actively. We first present the dynamical modeling of LCC, with a
focus on three fundamental scenarios: car-following, free-driving, and
Connected Cruise Control. Then, the analysis of controllability, observability,
and head-to-tail string stability reveals the feasibility and potential of LCC
in improving mixed traffic flow performance. Extensive numerical studies
validate that the capability of CAVs in dissipating traffic perturbations is
further strengthened when incorporating the information of the vehicles behind
into the CAV's control.
