Distributed inteligent vehicular environment: enabling ITS through programmable networks

• Grant number: 17/50361-0
• Support Opportunities: Regular Research Grants
• Start date: January 01, 2020
• End date: April 30, 2021
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Agreement: INRIA
• Principal Investigator: Christian Rodolfo Esteve Rothenberg
• Grantee: Christian Rodolfo Esteve Rothenberg
• Principal researcher abroad: Thierry Turletti
• Institution abroad: Université Nice Sophia Antipolis , France
• Host Institution: Faculdade de Engenharia Elétrica e de Computação (FEEC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• City of the host institution: Campinas

Abstract

Transportation systems are part of our society's critical I infrastructure and are expected to experience transformative changes as the Internet revolution unfolds. The automotive industry is a notable example: it has been undergoing disruptive transformations as vehicles transition from traditional unassisted driving to fully automated driving, and eventually to the self-driving model, Communication technology advancements such as support for vehicle-to-infrastructure (V21) and vehicle-to-vehicle (V2V) communication have been one of the key enablers of next-generation transportation services, also known as Intelligent Transport Systems (ITS). However, ITS services and applications pose significant challenges to the underlying communication and network infrastructure due to their stringent low latency, reliability, scalability, and geographic decentralization requirements. The drive associated team proposal aims at addressing such challenges by: (1) developing a programmable network control plane that will dynamically adjust to current environmental conditions and network characteristics to support ITS' scalability, quality of service (QoS), and decentralization requirements, and (2) applying the proposed distributed network control plane framework to ITS services and applications, such as road hazard warning, autonomous- and self-driving vehicles, and passenger-centric services (e.g., infotainment and video streaming). (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

YUAN, TINGTING; ROTHENBERG, CHRISTIAN ESTEVE; OBRACZKA, KATIA; BARAKAT, CHADI; TURLETTI, THIERRY. arnessing UAVs for Fair 5G Bandwidth Allocation in Vehicular Communication via Deep Reinforcement Learnin. IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT, v. 18, n. 4, p. 4063-4074, DEC 2021. (17/50361-0)

YUAN, TINGTING; NETO, WILSON DA ROCHA; ROTHENBERG, CHRISTIAN ESTEVE; OBRACZKA, KATIA; BARAKAT, CHADI; TURLETTI, THIERRY. Dynamic Controller Assignment in Software Defined Internet of Vehicles Through Multi-Agent Deep Reinforcement Learning. IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT, v. 18, n. 1, p. 585-596, MAR 2021. (17/50361-0)