  This paper addresses vehicle positioning, a topic whose importance has risen
dramatically in the context of future autonomous driving systems. While
classical methods that use GPS and/or beacon signals from network
infrastructure for triangulation tend to be sensitive to multi-paths and signal
obstruction, our method exhibits robustness with respect to such phenomena. Our
approach builds on the recently proposed Channel-SLAM method which first
enabled leveraging of multi-path so as to improve (single) vehicle positioning.
Here, we propose a cooperative mapping approach which builds upon the
Channel-SLAM concept, referred to here as Team Channel-SLAM. Team Channel-SLAM
not only exploits the stationary nature of many reflecting objects around the
vehicle, but also capitalizes on the multi-vehicle nature of road traffic. The
key intuition behind our method is the exploitation for the first time of the
correlation between reflectors around multiple neighboring vehicles. An
algorithm is derived for reflector selection and estimation, combined with a
team particle filter (TPF) so as to achieve high precision simultaneous
multiple vehicle positioning. We obtain large improvement over the
single-vehicle positioning scenario, with gains being already noticeable for
moderate vehicle densities, such as over 40% improvement for a vehicle density
as low as 4 vehicles in 132 meters' length road.
