  Connected autonomous vehicles (CAVs) can supplement the information from
their own sensors with information from surrounding CAVs for decision making
and control. This has the potential to improve traffic efficiency. CAVs face
additional challenges in their driving, however, when they interact with
human-driven vehicles (HDVs) in mixed-traffic environments due to the
uncertainty in human's driving behavior e.g. larger reaction times, perception
errors, etc. While a lot of research has investigated the impact of CAVs on
traffic safety and efficiency at different penetration rates, all have assumed
either perfect communication or very simple scenarios with imperfect
communication. In practice, the presence of communication delays and packet
losses means that CAVs might receive only partial information from surrounding
vehicles, and this can have detrimental effects on their performance. This
paper investigates the impact of CAVs on traffic efficiency in realistic
communication and road network scenarios (i.e. imperfect communication and
large-scale road network). We analyze the effect of unreliable communication
links on CAVs operation in mixed traffic with various penetration rates and
evaluate traffic performance in congested traffic scenarios on a large-scale
road network (the M50 motorway, in Ireland). Results show that CAVs can
significantly improve traffic efficiency in congested traffic scenarios at high
penetration rates. The scale of the improvement depends on communication
reliability, with a packet drop rate of 70% leading to an increase in traffic
congestion by 28.7% and 11.88% at 40% and 70% penetration rates respectively
compared to perfect communication.
