  Connectivity in ground vehicles allows vehicles to share crucial vehicle
data, such as vehicle acceleration, with each other. Using sensors such as
cameras, radars and lidars, on the other hand, the intravehicular distance
between a leader vehicle and a host vehicle can be detected, as well as the
relative speed. Cooperative Adaptive Cruise Control (CACC) builds upon ground
vehicle connectivity and sensor information to form convoys with automated car
following. CACC can also be used to improve fuel economy and mobility
performance of vehicles in the said convoy. In this paper, 3 car following
algorithms for fuel economy of CAVs are presented. An Adaptive Cruise Control
(ACC) algorithm was designed as the benchmark model for comparison. A
Cooperative Adaptive Cruise Control (CACC) was designed, which uses lead
vehicle acceleration received through V2V in car following. an Ecological
Cooperative Adaptive Cruise Control (Eco-CACC) model was developed that takes
the erratic lead vehicle acceleration as a disturbance to be attenuated. A High
Level (HL) controller was designed for decision making when the lead vehicle
was an erratic driver. Model-in-the-Loop (MIL) and Hardware-in-the-Loop (HIL)
simulations were run to test these car following algorithms for fuel economy
performance. The results show that the HL controller was able to attain a
smooth speed profile that consumed less fuel through using CACC and Eco-CACC
than its ACC counterpart when the lead vehicle was erratic.
