  Vehicle positioning is considered a key element in autonomous driving
systems. While conventional positioning requires the use of GPS and/or beacon
signals from network infrastructure for triangulation, they are sensitive to
multi-path and signal obstruction. However, recent proposals like the
Channel-SLAM method showed it was possible in principle to in fact leverage
multi-path to improve positioning of a single vehicle. In this paper, we derive
a cooperative Channel-SLAM framework, which is referred as Team Channel-SLAM.
Different from the previous work, Team Channel-SLAM not only exploits the
stationary nature of reflecting objects around the receiver to characterize the
location of a single vehicle through multi-path signals, but also capitalizes
on the multi-vehicle aspects of road traffic to further improve
positioning.Specifically, Team Channel-SLAM exploits the correlation between
reflectors around multiple neighboring vehicles to achieve high precision
multiple vehicle positioning. Our method uses affinity propagation clustering
and cooperative particle filter. The new framework is shown to give substantial
improvement over the single vehicle positioning situation.
