Journal Press India^®

Author Details ( * ) denotes Corresponding author

The growth of smart cities is accelerating. Information and communication technology (ICT) is digitally reshaping our surroundings. The environment, governance, energy, transportation, and other sectors are all impacted by the digital transition. To better understand how millennials are embracing autonomous vehicles (AV), which are the wave of the future of transportation, a systematic theoretical understanding of the approach of evolution of smart autonomous vehicles is presented. By 2035, autonomous driving could create $300 billion to $400 billion in revenue Millennials are a crucial target of this business because they are eager to accept new technologies and transportation options.

Keywords

Autonomous Vehicles, transportation, intelligence, ADAS, Safety

1. International Energy Agency. (2020). Global EV Outlook 2020. Retrieved from https://www.iea.org/reports/global-ev-outlook-2020


2. Singh, N., Singh, T. P., & Azzopardi, B. (2024). Advances in Autonomous Navigation Through Intelligent Technologies. In Title of the Book (Page No: 10-259). Apple Academic Press. (In press)


3. Sawa, M., Singh, N., & Ravi, R. (2023). A Secure And Efficient Approach to Smart City Initiatives. In Title of the Book (page numbers 97-241).  Future Connected Technologies. (In press)


4. Chauhan, P., Singh, N., & Prateek, M. (2022). An Efficient Path Planning Algorithm for Networked Robots using Modified Optimization Algorithm. In Title of the Book (page numbers 23-219).


5. Singh, N., Prateek, M., Chauhan, P., & Choudhury, T. (2022). An Optimized Self-Learning Algorithm for Autonomous Navigation in an Unknown Environment. Journal of Green Engineering, 2020, Volume(II)


6. Dhakal, T., Kangwon National University, & Min, K.-S. (Year of Publication). Macro Study of Global Electric Vehicle Expansion. Foresight STI Gov, 15, 67–73.


7. Elmenshawy, M., & Massoud, A. (2020). Modular Isolated DC-DC Converters for Ultra-Fast EV Chargers: A Generalized Modeling and Control Approach. Energies, 13, 2540.


8. Shibl, M., Ismail, L., & Massoud, A. (2021). Electric Vehicles Charging Management Using Machine Learning Considering Fast Charging and Vehicle-to-Grid Operation. Energies, 14, 6199.


9. Lytras, M. D., Visvizi, A., & Jussila, J. (2020). Social media mining for smart cities and smart villages research. Soft Comput, 24, 10983–10987.


10. Morvaj, B., Lugaric, L., & Krajcar, S. (2011). Demonstrating smart buildings and smart grid features in a smart energy city. In Proceedings of the 2011 3rd International Youth Conference on Energetics (IYCE), Leiria, Portugal, 7–9 July 2011.


11. Qaisar, S. M., & Alyamani, N. (2022). A Review of Charging Schemes and Machine Learning Techniques for Intelligent Management of Electric Vehicles in Smart Grid. Management of Smart Cities, 51–71.


12. Axelsson, K., & Granath, M. (2018). Stakeholders’ stake and relation to smartness in smart city development: Insights from a Swedish city planning project. Government Information Quarterly, 35, 693–702.


13. Shokravi, H., Shokravi, H., Bakhary, N., Heidar Rezaei, M., Koloor, S. S. R., & Petru, M. (2020). Vehicle-assisted techniques for health monitoring of bridges. Sensors (Basel), 20. (Under review).


14. Shokravi, H., Shokravi, H., Bakhary, N., Koloor, S. S. R., & Petru, M. (2020). Application of the Subspace-based Methods in Health Monitoring of the Civil Structures: A Systematic Review and Meta-analysis. Applied Science, 10, 3607.


15. Sotheany, N., & Nuthong, C. (2017). Vehicle classification using neural network. In Proceedings of the 2017 14th International Conference Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), Phuket, Thailand, 27–30 June 2017; IEEE: Piscataway, NJ, USA, pp. 443–446.


16. Siddiqui, N., & Husain, M. S. (2016). CTS: A credit based threshold system to minimize the dissemination of faulty data in vehicular adhoc networks. International Journal of Control Theory and Applications, 9, 8499–8508.


17. Kim, C.-W., Chang, K.-C., McGetrick, P. J., Inoue, S., & Hasegawa, S. (2017). Utilizing moving vehicles as sensors for bridge condition screening-A laboratory verification. Sensors and Materials, 29, 153–163.


18. Velazquez-Pupo, R., Sierra-Romero, A., Torres-Roman, D., Shkvarko, Y. V., Santiago-Paz, J., Gómez-Gutiérrez, D., ... Romero-Delgado, M. (2018). Vehicle detection with occlusion handling, tracking, and OC-SVM classification: A high-performance vision-based system. Sensors (Switzerland), 18, 374.


19. Ji, Q., Jin, B., Cui, Y., & Zhang, F. (2017). Using Mobile Signaling Data to Classify Vehicles on Highways in Real Time. State Key Laboratory of Computer Sciences, Institute of Software, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; Institute of Electrical and Electronics Engineers Inc., Piscataway, NJ, USA, pp. 174–179.


20. Sain, C., Banerjee, A., Biswas, P. K., & Padmanaban, S. (2020). A state-of-the-art review on solar-powered energy-efficient PMSM drive smart electric vehicle for sustainable development. In Advances in Greener Energy Technologies (pp. 231–258). Springer: Singapore.


21. Nisha, K. S., Gaonkar, D. N., & Jayalakshmi, N. S. (2023). Operation and control of multiple electric vehicle load profiles in bipolar microgrid with photovoltaic and battery energy systems. Journal of Energy Storage, 57, 106261.


22. Mohammed, S. S., Titus, F., Thanikanti, S. B., Sulaiman, S. M., Deb, S., & Kumar, N. M. (2022). Charge Scheduling Optimization of Plug-In Electric Vehicle in a PV Powered Grid-Connected Charging Station Based on Day-Ahead Solar Energy Forecasting in Australia. Sustainability, 14, 3498.