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Connectivity analysis of V2V communication with discretionary lane changing approach

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Abstract

Vehicular ad hoc network (VANET) has become an exigent domain of Intelligent Transport system (ITS). Providing efficient communication among rapidly moving vehicles is a challenging task in highway environment. This paper analysis the connectivity of High Speed Mobility and Lane Changing (HSMLC) based on Discretionary Lane Changing (DLC) approach for V2V environment. The Recurrent Neural Network System (RNN) which models a driver’s decision to perform a Discretionary Lane Changing (DLC) process on highways. The RNN system model the DLC decision making using Adaptive Cruise Control (ACC) computational model. The ACC mechanism defines and extends traditional cursive control based on the input metrics which are extracted from Highway Traffic Management System database (HTMS). The RNN was trained and tested with HTMS data collected from Tamilnadu highway of India with ACC properties. The result part reviews the proposed DLC trajectories by lane changing phases, connectivity probability during DLC and packet delivery rate. During 65Kmph with 100 vehicles, the DLC takes highly 4.3 to 5.2 s for lane changing process and during this moment the PDR and throughput of the networks are 62 to 75% and 31.7 to 39Kbps, respectively. The simulation work done by two different simulators such as SUMO—mobility simulator and NS2—network simulator.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Engineering College, Kovilpatti, Tamilnadu, India

    J. Naskath

  2. Department of Information Technology, National Engineering College, Kovilpatti, Tamilnadu, India

    B. Paramasivan

  3. Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, Jordan

    Zaid Mustafa & Hamza Aldabbas

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  1. J. Naskath
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  2. B. Paramasivan
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  3. Zaid Mustafa
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  4. Hamza Aldabbas
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Naskath, J., Paramasivan, B., Mustafa, Z. et al. Connectivity analysis of V2V communication with discretionary lane changing approach. J Supercomput 78, 5526–5546 (2022). https://doi.org/10.1007/s11227-021-04086-8

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