  Adaptive Cruise Control (ACC) is an Advanced Driver Assistance System (ADAS)
that enables vehicle following with desired inter-vehicular distances.
Cooperative Adaptive Cruise Control (CACC) is upgraded ACC that utilizes
additional inter-vehicular wireless communication to share vehicle states such
as acceleration to enable shorter gap following. Both ACC and CACC rely on
range sensors such as radar to obtain the actual inter-vehicular distance for
gap-keeping control. The range sensor may lose detection of the target, the
preceding vehicle, on curvy roads or steep hills due to limited angle of view.
Unfavourable weather conditions, target selection failure, or hardware issue
may also result in target detection loss. During target detection loss, the
vehicle following system usually falls back to Cruise Control (CC) wherein the
follower vehicle maintains a constant speed. In this work, we propose an
alternative way to obtain the inter-vehicular distance during target detection
loss to continue vehicle following. The proposed algorithm integrates
inter-vehicular communication, accurate vehicle localization, and a digital map
with lane center information to approximate the inter-vehicular distance.
In-lab robot following experiments demonstrated that the proposed algorithm
provided desirable inter-vehicular distance approximation. Although the
algorithm is intended for vehicle following application, it can also be used
for other scenarios that demand vehicles' relative distance approximation. The
work also showcases our in-lab development effort of robotic emulation of
traffic for connected and automated vehicles.
