On communication aspects of particle-based cooperative positioning in GPS-aided VANETs

Precise location services are seen as key enablers to future Intelligent Transport Systems (ITSs). Relying on Vehicleto-Vehicle (V2V) communication links, one promising solution consists in performing distributed Cooperative Positioning (CP). More specifically, Cooperative Awareness Message (CAM) broadcasts from neighboring vehicles (seen as "virtual anchors") are used to exchange positional information and to measure V2V radiolocation metrics such as the Received Signal Strength Indicator (RSSI). For the sake of fusing these non-linear hybrid data, Particle Filters (PFs) represent the required positional information by a set of particles with associated weights. However, in a jointly cooperative and distributed context, the transmission of explicit particle clouds (required by receiving neighbors to update their own location estimates) is hardly affordable under limited V2V channel capacity with typical numbers of particles. In this paper we thus combine and compare several solutions in terms of message representation and adaptive transmission policy so as to reduce simultaneously CAM overhead, channel congestion and computational complexity. Proposals are made at both signal processing level (parametric density approximation) and protocol level (jointly adaptive transmission payload, power and rate), showing no impact on channel load in congested scenarios and negligible CP accuracy degradation in comparison with standard CAM transmission at critical rates.