2019/03/04 by Hossein Nourkhiz Mahjoub, Behrad Toghi, Mahjoub, Hossein Nourkhiz +5
Computer Science · Engineering · #Autonomous Vehicle Technology and Safety #FOS: Computer and information sciences #FOS: Electrical engineering #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Signal Processing (eess.SP) #Vehicular Ad Hoc Networks (VANETs) #cs.LG #eess.SP #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1903.01576
Accepted for Oral Presentation at the 13th IEEE Systems Conference (SysCon 2019)
openalex publication_date 2019/03/04 · arxiv created 2019/03/06 · arxiv updated 2019/03/07 · openalex created_date 2021/08/16 · openalex updated_date 2026/08/04
Scalable communication is of utmost importance for reliable dissemination of time-sensitive information in cooperative vehicular ad-hoc networks (VANETs), which is, in turn, an essential prerequisite for the proper operation of the critical cooperative safety applications. The model-based communication (MBC) is a recently-explored scalability solution proposed in the literature, which has shown a promising potential to reduce the channel congestion to a great extent. In this work, based on the MBC notion, a technology-agnostic hybrid model selection policy for Vehicle-to-Everything (V2X) communication is proposed which benefits from the characteristics of the non-parametric Bayesian inference techniques, specifically Gaussian Processes. The results show the effectiveness of the proposed communication architecture on both reducing the required message exchange rate and increasing the remote agent tracking precision.