A Cache Strategy for Intelligent Transportation System to Connected Autonomous Vehicles

Author(s)

Lobato Junior, Wellington ; de Souza, Allan M. ; Peixoto, Maycon L. M. ; Rosario, Denis ; Villas, Leandro ; IEEE

• Document type: Journal article
• Source: 2020 IEEE 92ND VEHICULAR TECHNOLOGY CONFERENCE (VTC2020-FALL); v. N/A, p. 5-pg., 2020-01-01.

Abstract

Traffic congestion is a major problem in metropolitan areas, which inevitably leads to substantial social and economic impacts. In the Connected Autonomous Vehicles (CAVs) context, Intelligent Transportation System (ITS) addresses routing techniques for building an efficient transportation system in an urban environment. In order to improve traffic management, CAVs use real-time traffic data to disseminate faster routes for vehicles. Meanwhile, Cloud Computing is used to manage the traffic congestion situation, but it is not a suitable option for low -latency requirements of autonomous vehicles. Fog-based approaches dealing with traffic congestion found in the literature do not consider the use of caching for a routing scheme. Therefore, we propose a reliable caching mechanism for autonomous vehicle path planning based on Fog Computing, which is called ReCall. ReCall caches real-time traffic information from different regions to dynamically perform route recommendations. The results have shown that ReCall is able to reduce travel time and emissions. (AU)

• FAPESP's process: 18/23126-3 - Data Orchestration for Urban Computing through Fog Computing
• Grantee: Maycon Leone Maciel Peixoto
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 15/24494-8 - Communications and processing of big data in cloud and fog computing
• Grantee: Nelson Luis Saldanha da Fonseca
• Support Opportunities: Research Projects - Thematic Grants