Abstract: Infotainment system potentially contributes towards controlling accident fatalities in the era of Internet-of-Vehicles (IoV). Review of existing system is carried out to find that irrespective of various methods towards infotainment system, the quality of data being retrieved as well as issues associated with power and traffic congestion in vehicular communication is still an impending challenge. Therefore, this manuscript introduces a novel predictive scheme that offers enriched set of information from the environment to assists in decision making. Reinforcement learning is adopted for controlling traffic signal and power while the proposed system introduce augmented Long Short Term Memory scheme in order to predict the best possible traffic scenario for assisting the infotainment system to make a precise decision. The simulation is carried out for proposed system with existing learning schemes to find out proposed scheme offers better performance in every respect over challenging scene of an IoV.

Reshma S and Chetanaprakash, “Framework of Infotainment using Predictive Scheme for Traffic Management in Internet-of-Vehicle” International Journal of Advanced Computer Science and Applications(IJACSA), 13(4), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130473

@article{S2022,
title = {Framework of Infotainment using Predictive Scheme for Traffic Management in Internet-of-Vehicle},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130473},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130473},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {4},
author = {Reshma S and Chetanaprakash}
}