Velocity Awareness in Vehicular Networks via Sparse Recovery

In this paper, we study the problem of velocity estimation in vehicular networks by exploiting the sparsity of vehicle velocity trajectory. First, we exploit the sparsity of vehicular velocity trajectories to reduce the beaconing load on the channel. To this end, we propose a sublayer that reduces the number of transmitted packets through a superframe. At each superframe, each vehicle transmits a few samples of its velocity as well as an encoded measurement of its past velocities. Sparse recovery of each velocity vector is performed at the receiver. We propose the use of repetitions at the medium access control (MAC) layer. Moreover, we extend our scheme into a streaming estimation system without the superframe concept. The streaming system uses a sliding window concept. We thoroughly study the estimation error of the streaming system, and we propose an algorithm to find the best solution over a streaming sliding window. The proposed superframe and streaming schemes are tested with real velocity traces collected in the city of Toronto to capture the performance in the city and highway conditions. Experiment results show that the proposed schemes significantly reduce the number of exchanged packets while preserving the velocity information with an excellent accuracy at the receiver. We also demonstrate how past velocity can be used to enhance vehicle localization accuracy.