Vehicular Edge Computing Based Driver Recommendation System Using Federated Learning
01 Dec 2020-pp 675-683
TL;DR: 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.
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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.
Abstract: Mobile edge computing (MEC) has been envisioned as a promising paradigm that could effectively enhance the computational capacity of wireless user devices (WUDs) and quality of experience of mobile applications. One of the most crucial issues of MEC is computation offloading, which decides how to offload WUDs’ tasks to edge severs for further intensive computation. Conventional mathematical programming-based offloading approaches could face troubles in dynamic MEC environments due to the time-varying channel conditions (caused primarily by WUD mobility). To address the problem, reinforcement learning (RL) based offloading approaches have been proposed, which develop offloading policies by mapping MEC states to offloading actions. However, these approaches could fail to converge in large-scale MEC due to the exponentially-growing state and action spaces. In this article, we propose a novel online computation offloading approach that could effectively reduce task latency and energy consumption in dynamic MEC with large-scale WUDs. First, a RL-based computation offloading and energy transmission algorithm is proposed to accelerate the learning process. Then, a joint optimization method is adopted to develop the allocating algorithm, which obtains near-optimal solutions for energy and computation resources allocation. Simulation results show that the proposed approach can converge efficiently and achieve significant performance improvements over baseline approaches.
7 citations
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.
5 citations
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.
Abstract: Abstract In recent years, with the rapid growth of edge data, the novel cloud-edge collaborative architecture has been proposed to compensate for the lack of data processing power of traditional cloud computing. On the other hand, on account of the increasing demand of the public for data privacy, federated learning has been proposed to compensate for the lack of security of traditional centralized machine learning. Deploying federated learning in cloud-edge collaborative architecture is widely considered to be a promising cyber infrastructure in the future. Although each cloud-edge collaboration and federated learning is hot research topic respectively at present, the discussion of deploying federated learning in cloud-edge collaborative architecture is still in its infancy and little research has been conducted. This article aims to fill the gap by providing a detailed description of the critical technologies, challenges, and applications of deploying federated learning in cloud-edge collaborative architecture, and providing guidance on future research directions.
4 citations
01 Jan 2022
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.
Abstract: : With the emerge of Vehicle-to-everything (V2X) communication, vehicles and other road users can perform Collective Perception (CP), whereby they exchange their individually detected environment to increase the collective awareness of the surrounding environment. To detect and classify the surrounding environmental objects, preprocessed sensor data (e.g., point-cloud data generated by a Lidar) in each vehicle is fed and classified by onboard Deep Neural Networks (DNNs). The main weakness of these DNNs is that they are commonly statically trained with context-agnostic data sets, limiting their adapt-ability to specific environments. This may eventually prevent the detection of objects, causing safety disasters. Inspired by the Federated Learning (FL) approach, in this work we 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.
2 citations
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.
Abstract: Autonomous vehicles (AVs) are getting popular because of their usage in a wide range of applications like delivery systems, self-driving taxis, and ambulances. AVs utilize the power of machine learning (ML) and deep learning (DL) algorithms to improve their self-driving learning experiences. The sudden surge in the number of AVs raises the need for distributed learning ecosystem to optimize their self-driving experiences at a rapid pace. Toward this goal, federated learning (FL) benefits, which can create a distributed learning environment for AVs. But, the traditional FL transfers the raw input data directly to a server, which leads to privacy concerns among the end-users. The concept of blockchain helps us to protect privacy, but it requires additional computational infrastructure. The extra infrastructure increases the operational cost for the company handling and maintaining the AVs. Motivated by this, in this paper, the authors introduced the concept of gradient encryption in FL, which preserves the users’ privacy without the additional computation requirements. 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. For performance evaluation, the authors have built a German traffic sign recognition system using a convolutional neural network (CNN) algorithm-based classification system and GeFL. The simulation process is carried out over a wide range of input parameters to analyze the performance at scale. Simulation results of GeFL outperform the conventional FL-based algorithms in terms of accuracy, i.e., 2% higher. Also, the amount of data transferred among the devices in the network is nearly three times less in GeFL compared to the traditional FL.
1 citations
References
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07 May 2016
TL;DR: The main contribution is the insight that byproducts of an existing activity prediction algorithm can be used to model those causal relationships in routines and show that the modeled routines are meaningful-that they are predictive of people's actions and that the Models provide insights about the routines that match findings from previous research.
Abstract: Human routines are blueprints of behavior, which allow people to accomplish purposeful repetitive tasks at many levels, ranging from the structure of their day to how they drive through an intersection. People express their routines through actions that they perform in the particular situations that triggered those actions. An ability to model routines and understand the situations in which they are likely to occur could allow technology to help people improve their bad habits, inexpert behavior, and other suboptimal routines. However, existing routine models do not capture the causal relationships between situations and actions that describe routines. Our main contribution is the insight that byproducts of an existing activity prediction algorithm can be used to model those causal relationships in routines. We apply this algorithm on two example datasets, and show that the modeled routines are meaningful-that they are predictive of people's actions and that the modeled causal relationships provide insights about the routines that match findings from previous research. Our approach offers a generalizable solution to model and reason about routines.
136 citations
18 Nov 2011
TL;DR: This paper review the field of driver behavior and intent prediction, with a specific focus on tactical maneuvers, as opposed to operational or strategic maneuvers, to provide insights into the scope of the problem.
Abstract: Drawing upon fundamental research in human behavior prediction, recently there has been a research focus on how to predict driver behaviors. In this paper we review the field of driver behavior and intent prediction, with a specific focus on tactical maneuvers, as opposed to operational or strategic maneuvers. The aim of a driver behavior prediction system is to forecast the trajectory of the vehicle prior in real-time, which could allow a Driver Assistance System to compensate for dangerous or uncomfortable circumstances. This review provides insights into the scope of the problem, as well as the inputs, algorithms, performance metrics, and shortcomings in the state-of-the-art systems.
131 citations
TL;DR: F-GVRP is modelled to minimize both route cost and fuel consumption using goal programming and it is observed that better routing plan with minimum fuel consumption can be achieved under varying speed environment.
Abstract: A bi-objective Fuel efficient Green Vehicle Routing Problem (F-GVRP) with varying speed constraint is discussed in this paper as an extension of Green Vehicle Routing Problem (G-VRP). F-GVRP is modelled to minimize both route cost and fuel consumption using goal programming. The problem is solved using Particle Swarm Optimization with Greedy Mutation Operator and Time varying acceleration coefficient (TVa-PSOGMO). The objective of this paper is to study the behaviour of F-GVRP under varying speed environment and its impact on the route cost and fuel consumption. Experiments are conducted with constant and varying speed constraints and it is observed that better routing plan with minimum fuel consumption can be achieved under varying speed environment.
111 citations
TL;DR: In this paper, a machine learning-based approach to identify driving-induced stress patterns was presented, where electroencephalograph (EEG) signals are utilized as the physiological signals.
110 citations
29 Oct 2015
TL;DR: This study focuses on ECG monitoring that can now be performed with minimally invasive wearable patches and sensors, to develop an efficient and robust mechanism for accurate stress identification.
Abstract: Physiological sensor analytics is becoming an important tool to monitor health as the availability of sensor-enabled portable, wearable, and implantable devices becomes ubiquitous in the growing Internet of Things (IoT). Physiological multi-sensor studies have been conducted previously to detect stress. In this study, we focus on ECG monitoring that can now be performed with minimally invasive wearable patches and sensors, to develop an efficient and robust mechanism for accurate stress identification. A unique aspect of our research is personalized individual stress analysis including three stress levels: low, medium and high. Using machine learning algorithms from the ECG signals alone, we could achieve 88.24% accuracy in detecting the three classes of stress. We also find that high stress can be successfully detected for a person in comparison to his or her rest period with 100% accuracy.
106 citations