Dynamic vehicle cloud for autonomous vehicle application support

• Grant number: 19/19105-3
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: July 01, 2020
• End date: February 22, 2024
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Principal Investigator: Leandro Aparecido Villas
• Grantee: Wellington Viana Lobato Junior
• Host Institution: Instituto de Computação (IC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Associated research grant: 15/24494-8 - Communications and processing of big data in cloud and fog computing, AP.TEM

Abstract

Autonomous vehicles have the potential to revolutionize the entire transportation industry. The development of sensor technologies enables a new era in autonomous driving. However, due to the limitations of these sensors and the restrictions on processing and storage, autonomous vehicles tend to make bad decisions and cause serious disasters. In this context, vehicular cloud emerges as an alternative to compensate for the deficiencies of these sensors and provide processing capability, as well as increase the reliability of actions taken by vehicles, providing basic infrastructure support for autonomous driving, including storage and distributed computing. This project aims to implement a prototype of a dynamic VCC to provide data processing support for autonomous vehicle applications. The prototype will be built incrementally and will have two main state-of-the-art elements in the area of autonomous vehicle networks, namely: i) the creation of a dynamic VCC prototype based on the mobility standard; and ii) collection, analysis and characterization of heterogeneous data, which has spatiotemporal validity, to assist in the decision-making process of VAs. The solutions to be developed have the potential to advance state-of-the-art and help foster the implementation of autonomous vehicle solutions. (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)

LOBATO, WELLINGTON; DA COSTA, JOAHANNES B. D.; DE SOUZA, ALLAN M.; ROSARIO, DENIS; SOMMER, CHRISTOPH; VILLAS, LEANDRO A.; IEEE. FLEXE: Investigating Federated Learning in Connected Autonomous Vehicle Simulations. 2022 IEEE 96TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2022-FALL), v. N/A, p. 5-pg., 2022-01-01. (19/19105-3, 15/24494-8, 21/13780-0, 18/16703-4)

PEIXOTO, M. L. M.; MOTA, E.; MAIA, A. H. O.; LOBATO, W.; SALAHUDDIN, M. A.; BOUTABA, R.; VILLAS, L. A.. FogJam: A Fog Service for Detecting Traffic Congestion in a Continuous Data Stream VANET. Ad Hoc Networks, v. 140, p. 15-pg., 2023-03-01. (18/23126-3, 19/19105-3, 15/24494-8)

LOBATO, WELLINGTON; MENDES, PAULO; ROSARIO, DENIS; CERQUEIRA, EDUARDO; VILLAS, LEANDRO A. A.. Redundancy Mitigation Mechanism for Collective Perception in Connected and Autonomous Vehicles. FUTURE INTERNET, v. 15, n. 2, p. 17-pg., 2023-02-01. (19/19105-3)