Proceedings ArticleDOI
Vehicular Edge Computing Based Driver Recommendation System Using Federated Learning
Jayant Vyas,Debasis Das,Sajal K. Das +2 more
- pp 675-683
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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Journal ArticleDOI
An Efficient Online Computation Offloading Approach for Large-Scale Mobile Edge Computing via Deep Reinforcement Learning
TL;DR: In this paper , a novel online computation offloading approach that could effectively reduce task latency and energy consumption in dynamic MEC with large-scale wireless user devices (WUDs) was proposed to accelerate the learning process.
Journal ArticleDOI
Federated recommenders: methods, challenges and future
TL;DR: This research summarizes the current limitations, highlights the areas that need improvements, and presents future paths for the development of robust federated recommenders that can handle the challenges of federated learning and, at the same time, generate high-quality recommendations.
Journal ArticleDOI
Federated learning in cloud-edge collaborative architecture: key technologies, applications and challenges
Guanming Bao,Pingbo Guo +1 more
TL;DR: In this paper , the authors provide a detailed description of the critical technologies, challenges, and applications of federated learning in cloud-edge collaborative architecture, and provide guidance on future research directions.
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.
Journal ArticleDOI
<i>GeFL</i>: Gradient Encryption-Aided Privacy Preserved Federated Learning for Autonomous Vehicles
TL;DR: In this paper , the authors introduced the concept of gradient encryption in federated learning (FL), which preserves the users' privacy without the additional computation requirements, and the computational power present in the edge devices helps to fine tune the local model and encrypt the input data to preserve privacy without any drop in performance.
References
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Proceedings ArticleDOI
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Instantaneous Fuel Consumption Estimation Using Smartphones
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