Computational Intelligence and Adaptation in VANETs: Current Research and New Perspectives

The increasing number of moving vehicles along roads and the lack of supporting infrastructure is a wellestablished problem. Major consequences are augmenting of traffic jams, accidents, fuel consumption and pollution. Vehicular Ad hoc NETworks (VANETs) represent opportunities to deal with the aforementioned problems. In VANETS, efficiency and safety to applications are provided using communication support. In efficiency applications, each vehicle is aware of its location. Using this information and communication support, vehicles collaborate to reduce travel time and to improve mobility. In contrast, safety applications aim to reduce or even avoid accidents, and must obey strong timing constraints. In this context, VANETs applications can benefit from Computational Intelligence (CI) and adaptive approaches to implement the required demands. Thus, the contribution of this paper is twofold: $( i)$ we discuss how VANETs can benefit from CI and Artificial Intelligence techniques to make transportation networks more efficient regarding to safety applications, and, $( ii)$ we report our current work and new directions in the development of efficiency applications to VANETs using adaptation and CI techniques.

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