There has been a tremendous growth in the transportation sector over the last decade. Importantly, this has also resulted in the need for communication between vehicles. However, the biggest disadvantage has been the high-speed nature of the vehicles which inhibits the stability of communication. A cluster-based technique wherein all vehicles within a region are grouped together with different levels of the hierarchy have been well investigated. However, such technique fails with dynamic variation in the traffic environment. In this paper, an adaptive clustering technique is proposed based on image-based real-time traffic identification. The novelty lies in proposing an inception v3 network with deep CNN (IP-DNN) for image-based traffic identification which then serves as the basis for selecting the best clustering technique. It has been found that such an adaptive mechanism results in up to 25% faster cluster selection as compared to static techniques. This is a significant result that provides the necessary impetus for developing real-time V2V (vehicle to vehicle) communication across varying traffic environments.


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    Title :

    An Adaptive Clustering Technique Based on Image-Based Traffic Identification for Real-Time V2V Communication


    Contributors:


    Publication date :

    2018-06-01


    Size :

    611942 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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