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An intelligent energy management and traffic predictive model for autonomous vehicle systems

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Abstract

In recent times, the utilization of autonomous vehicles (AVs) has been significantly increased over the globe. It is because of the tremendous rise in familiarity and the usage of artificial intelligence approaches in distinct application areas. Though AVs offer several benefits like congestion control, accident prevention, and so on, energy management and traffic flow prediction (TFP) remain a challenging issue. This paper concentrates on the design of intelligent energy management and TFP (IEMTFP) technique for AVs using multi-objective reinforced whale optimization algorithm (RWOA) and deep learning (DL). The proposed model involves an energy management module using fuzzy logic system to reach the specified engine torque with respect to different measures. For optimal tuning of the variables involved in the fuzzy logic membership functions (MFs), RWOA is employed to further reduce the energy utilization. Besides, the proposed model uses a DL-based bidirectional long short-term memory (Bi-LSTM) technique to perform TFP. For validating the efficacy of the IEMTFP technique, an extensive experimental validation is carried out. The resultant values ensured the goodness of the IEMTFP model in terms of energy management and TFP.

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Acknowledgments

The authors would like to thank the anonymous reviewers for their critical and constructive comments, their thoughtful suggestions have helped improve this paper substantially. Irina V. Pustokhina is thankful to the Department of Entrepreneurship and Logistics, Plekhanov Russian University of Economics, Moscow, Russia. K. Shankar would like to thank RUSA PHASE 2.0, Dept. of Edn. Govt. of India.

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Authors and Affiliations

  1. Department of Information Technology, VR Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India

    Suneetha Manne

  2. Computer Science and Engineering, Vignans Institute of Information Technology (Autonomous), Visakhapatnam, India

    E. Laxmi Lydia

  3. Department of Entrepreneurship and Logistics, Plekhanov Russian University of Economics, 117997, Moscow, Russia

    Irina V. Pustokhina

  4. Department of Logistics, State University of Management, 109542, Moscow, Russia

    Denis A. Pustokhin

  5. Department of Electronics and Communication Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Tamilnadu, 626126, India

    Velmurugan Subbiah Parvathy

  6. Department of Computer Applications, Alagappa University, Karaikudi, India

    K. Shankar

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  1. Suneetha Manne
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  2. E. Laxmi Lydia
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  3. Irina V. Pustokhina
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  4. Denis A. Pustokhin
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  5. Velmurugan Subbiah Parvathy
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  6. K. Shankar
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Correspondence to K. Shankar.

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The authors declare that they have no conflict of interest. The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.

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Communicated by Vicente Garcia Diaz.

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Manne, S., Lydia, E.L., Pustokhina, I.V. et al. An intelligent energy management and traffic predictive model for autonomous vehicle systems. Soft Comput 25, 11941–11953 (2021). https://doi.org/10.1007/s00500-021-05614-7

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  • DOI: https://doi.org/10.1007/s00500-021-05614-7

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