Proceedings ArticleDOI
Vehicular Edge Computing Based Driver Recommendation System Using Federated Learning
Jayant Vyas,Debasis Das,Sajal K. Das +2 more
- pp 675-683
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TLDR
In this paper, the authors used Long Short-Term Memory Fully Convolutional Network to predict the corresponding stress level of the driver and 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.Abstract:
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.read more
Citations
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An Efficient Online Computation Offloading Approach for Large-Scale Mobile Edge Computing via Deep Reinforcement Learning
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Federated recommenders: methods, challenges and future
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Proceedings ArticleDOI
Situational Collective Perception: Adaptive and Efficient Collective Perception in Future Vehicular Systems
Ahmad Syazli Mohd Khalil,Tobias Meuser,Yassin Alkhalili,Antonio Fernandez,Lukas Stäcker,Ralf Steinmetz +5 more
TL;DR: inspired by the Federated Learning (FL) approach, this work tailor a collective perception architecture, introducing Situational Collective Perception (SCP) based on dynamically trained and situational DNNs, and enabling adaptive and efficient collective perception in future vehicular networks.
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<i>GeFL</i>: Gradient Encryption-Aided Privacy Preserved Federated Learning for Autonomous Vehicles
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