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AI for Good Innovate for Impact



               4      Sequence Diagram                                                                             Transport  4.10: Intelligent














































               5      References

               [1]  ITU. AI for Good–Innovate for Impact Report 2024, Use Case 36: “Bidirectional Telecom-
                    to-Grid and Telecom-to-Vehicle Energy Exchange for Net-Zero.” Accessed June 19, 2025.
                    https:// demo .ifgict .org/ wp -content/ uploads/ 2024/ 09/ ITU -AI -for -Good -Innovate -final
                    -report -1 .pdf.
               [2]  Zhu, Z., S. Gupta, A. Gupta, and M. Canova. "A Deep Reinforcement Learning Framework
                    for Eco-Driving in Connected and Automated Hybrid Electric Vehicles." IEEE Transactions
                    on Vehicular Technology 73, no. 2 (February 2024): 1713–1725. https:// doi .org/ 10 .1109/
                    TVT .2023 .3318552.
               [3]  Comma.ai. "Openpilot: An Open-Source Driving Automation System." Accessed June
                    19, 2025. https:// comma .ai/ openpilot.
               [4]  Kerbel, L., B. Ayalew, A. Ivanco, and K. Loiselle. "Driver Assistance Eco-driving and
                    Transmission Control with Deep Reinforcement Learning." In 2022 American Control
                    Conference (ACC), 2409–2415. Atlanta, GA, USA, 2022. https:// doi .org/ 10 .23919/
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               [5]  Holden, J., N. Reinicke, and J. Cappellucci. "Route E: A Vehicle Energy Consumption
                    Prediction Engine." SAE International Journal of Advanced & Current Practices in Mobility
                    2, no. 5 (2020): 2760–2767. https:// doi .org/ 10 .4271/ 2020 -01 -0939.
               [6]  Lombardi, Federico, et al. "RAMP: Stochastic Simulation of User-Driven Energy Demand
                    Time Series." Journal of Open Source Software 9, no. 98 (2024). https:// doi .org/ 10 .21105/
                    joss .06418.







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