A vehicle-to-infrastructure communication based algorithm for urban traffic control

We present in this paper a new algorithm for urban traffic light control with mixed traffic (communicating and non communicating vehicles) and mixed infrastructure (equipped and unequipped junctions). We call equipped junction here a junction with a traffic light signal (TLS) controlled by a road side unit (RSU). On such a junction, the RSU manifests its connectedness to equipped vehicles by broadcasting its communication address and geographical coordinates. The RSU builds a map of connected vehicles approaching and leaving the junction. The algorithm allows the RSU to select a traffic phase, based on the built map. The selected traffic phase is applied by the TLS; and both equipped and unequipped vehicles must respect it. The traffic management is in feedback on the traffic demand of communicating vehicles. We simulated the vehicular traffic as well as the communications. The two simulations are combined in a closed loop with visualization and monitoring interfaces. Several indicators on vehicular traffic (mean travel time, ended vehicles) and IEEE 802.11p communication performances (end-to-end delay, throughput) are derived and illustrated in three dimension maps. We then extended the traffic control to a urban road network where we also varied the number of equipped junctions. Other indicators are shown for road traffic performances in the road network case, where high gains are experienced in the simulation results.Comment: 6 page

An Overview of Vehicle-to-Infrastructure Communication Technology

As a part of solutions to reduce problems associated with transportation in cities, technologies can have noticeable impacts. Due to efficiency and low costs, innovative transportation technologies can reshape and improve human’s transportation. This research aims to explore Vehicle-to-Infrastructure communication technology (V2I) and its benefits to safety, mobility, and environment. In addition, it explores the planning aspect of deploying V2I technology and its opportunities, challenges and concerns, and implication to communities. The research will also look at several case studies including pilot projects that have been taking place in the United States and studies that have been done to have a better understanding of the current situation of V2I technology and its future needs. Advisor: Rodrigo Cantarer

Optimal Content Prefetching in NDN Vehicle-to-Infrastructure Scenario

Data replication and in-network storage are two basic principles of the Information Centric Networking (ICN) framework in which caches spread out in the network can be used to store the most popular contents. This work shows how one of the ICN architectures, the Named Data Networking (NDN), with content pre-fetching can maximize the probability that a user retrieves the desired content in a Vehicle-to-Infrastructure scenario. We give an ILP formulation of the problem of optimally distributing content in the network nodes while accounting for the available storage capacity and the available link capacity. The optimization framework is then leveraged to evaluate the impact on content retrievability of topology- and network-related parameters as the number and mobility models of moving users, the size of the content catalog and the location of the available caches. Moreover, we show how the proposed model can be modified to find the minimum storage occupancy to achieve a given content retrievability level. The results obtained from the optimization model are finally validated against a Name Data Networking architecture through simulations in ndnSIM

Radar-assisted Predictive Beamforming for Vehicle-to-Infrastructure Links

In this paper, we propose a radar-assisted predictive beamforming design for vehicle-to-infrastructure (V2I) communication by relying on the joint sensing and communication functionalities at road side units (RSUs). We present a novel extended Kalman filtering (EKF) framework to track and predict kinematic parameters of the vehicle. By exploiting the radar functionality of the RSU we show that the communication beam tracking overheads can be drastically reduced. Numerical results have demonstrated that the proposed radar-assisted approach significantly outperforms the communication-only feedback based technique in both the angle tracking and the downlink communication.Comment: 6 pages, 3 figures, accepted by IEEE ICC 2020. arXiv admin note: substantial text overlap with arXiv:2001.0930

Assessing Vehicular Density Estimation Using Vehicle-to-Infrastructure Communications

©2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Vehicle density is one of the main metrics used for assessing the road traffic conditions. In this paper, we present a solution to estimate the density of vehicles that has been specially designed for Vehicular Networks. Our proposal allows Intelligent Transportation Systems to continuously estimate the vehicular density by accounting for the number of beacons received per Road Side Unit, as well as the roadmap topology. Simulation results indicate that our approach accurately estimates the vehicular density, and therefore automatic traffic controlling systems may use it to predict traffic jams and introduce countermeasures. Index Terms—Vehicular Networks, vehicular density estimation, Road Side Unit, VANETs.This work was partially supported by the Ministerio de Ciencia e Innovación, Spain, under Grant TIN2011-27543-C03-01.Barrachina Villalba, J.; Fogue, M.; Garrido, P.; Martínez, FJ.; Cano Escribá, JC.; Tavares De Araujo Cesariny Calafate, CM.; Manzoni, P. (2013). Assessing Vehicular Density Estimation Using Vehicle-to-Infrastructure Communications. IEEE. https://doi.org/10.1109/WoWMoM.2013.6583416