Vehicular Edge Computing based Driver Recommendation System using Federated Learning
Driver Stress and Behavior prediction is a significant feature of the Advanced Driver Assistance System. This system can improve driving safety by alerting the driver to the danger of unsafe or risky driving conditions. In this paper, we analyzed historical trip data to calculate the driving stress and its impact on different driving behavior. We used Long Short-Term Memory Fully Convolutional Network to predict the corresponding stress level of the driver. We further established a relationship between stress and driving behavior and developed an intelligent recommendation system for cab companies to recommend the driver for a subsequent trip. To meet the demand for Artificial Intelligence in the Intelligent Transportation System, we leverage Federated Learning in Vehicular Edge Computing in the proposed system architecture. It enables Road Side Units to do all computing of data on it. The model has been tested on the UAH-DriveSet dataset. We observed that the proposed model predicts the stress with an accuracy of 95% and assists in enhancing the driving quality and experience.
J. Vyas et al., "Vehicular Edge Computing based Driver Recommendation System using Federated Learning," Proceedings - 2020 IEEE 17th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2020, pp. 675 - 683, Dec 2020.
The definitive version is available at https://doi.org/10.1109/MASS50613.2020.00087
2020 IEEE 17th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2020
Center for High Performance Computing Research
Keywords and Phrases
Driver stress and behavior; Federated learning; Recommendation system; Vehicular edge computing
International Standard Book Number (ISBN)
Article - Conference proceedings
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01 Dec 2020