A Resilient and Scalable Flocking Scheme in Autonomous Vehicular Networks

  • Authors:

  • Naixue Xiong;Athanasios V. Vasilakos;Laurence T. Yang;Witold Pedrycz;Yan Zhang;Yingshu Li

  • Affiliations:

  • Department of Computer Science, Georgia State University, Atlanta, USA;Depa. of Comp. and Teleco. Engi., University of Western Macedonia, Kozani, Greece;Department of Computer Science, St. Francis Xavier University, Antigonish, Canada;Depa. of Electrical and Computer Engineering, University of Alberta Edmonton, Edmonton, Canada;Simula Research Laboratory, Lysaker, Norway 1325;Department of Computer Science, Georgia State University, Atlanta, USA

  • Venue:
  • Mobile Networks and Applications
  • Year:
  • 2010

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Abstract

Vehicular Ad hoc NETworks (VANET) is emerging as a highly promising technology, which aims to provide road safety, environment protection and personal-oriented services. The vehicle ad hoc wireless communications form an indispensable part of truly ubiquitous communications networking. VANET is formed by spontaneously moving autonomous vehicles with the self-organization and self-management capability. In this paper, we focus on the decentralized coordination of multiple unmanned vehicles such that they can freely move and adaptively cooperate in a complex environment. During this procedure, flocking is one of the key operations and requirements. Here, flocking refers to the formation and maintenance of a desired pattern by a group of mobile vehicles without collision during movement. We propose a resilient and scalable flocking scheme for a group of vehicles, which follows the leader---followers moving pattern. In the absence of obstacles, a collision avoidance algorithm is presented to maintain a desired distance among vehicles. This will ensure information completeness and is significant in certain mission critical situations without collision between a unmanned vehicle and its neighboring vehicles. In the presence of obstacles in an environment, this algorithm is able to avoid collision between a vehicle and its neighbor (either a neighboring vehicle or a neighboring obstacle). Theoretical proof has been presented to demonstrate the effectiveness and correctness of the algorithm to guarantee collision-free. In addition, with increasing number of vehicles, the performance of the proposed flocking scheme performs without increasing the processing overhead, which demonstrates the desirable scalability.