Vehicle Localization via Cooperative Channel Mapping

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 multi-paths and signal obstruction, our method exhibits robustness with respect such phenomena. Our approach builds on recently proposed Channel-SLAM which first enabled leveraging multi-path so as improve (single) positioning. Here, we propose cooperative mapping upon concept, referred here Team Channel-SLAM. not only exploits stationary nature many reflecting objects around vehicle, but also capitalizes multi-vehicle road traffic. The key intuition behind is exploitation time correlation between reflectors multiple neighboring vehicles. An algorithm derived reflector selection estimation, combined team particle filter (TPF) achieve high precision simultaneous We obtain large improvement over single-vehicle positioning scenario, gains being already noticeable moderate densities, 40 xmlns:xlink="http://www.w3.org/1999/xlink">% density low 4 vehicles 132 meters' length road.