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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01 Jun 2018
TL;DR: A fatigue-aware model is introduced for determining the optimal schedule for a driver while maintaining an acceptable level of alertness as well as abiding by time windows and hours of service (HOS) regulations.
Abstract: In the United States, approximately 4000 fatalities due to truck and bus crashes occur each year. Of these, up to 20% are estimated to involve fatigued drivers [48]. However, no model currently exists that incorporates a measure of drowsiness or fatigue into the Truck Driver Scheduling Problem (TDSP). We introduce a fatigue-aware model for determining the optimal schedule for a driver while maintaining an acceptable level of alertness as well as abiding by time windows and hours of service (HOS) regulations. Additionally, we examine a shortcoming in existing regulations, specifically related to assumptions made about the rest and alertness of a driver at the start of the workweek.
23 citations
11 Mar 2019
TL;DR: This paper explores historical trip data to compute the driving stress and its impact on various driving behavioral features, captured through vehicle-mounted GPS and inertial sensors and develops a trip recommendation system for cab drivers to avoid stress driving.
Abstract: The growth in the market for cab companies like Uber has opened the door to high-income options for drivers. However, in order to boost their income, drivers many a time resort to accepting trips which increases their stress resulting in poor driving quality and accidents in serious cases. Every driver handles stress differently and the trip recommendation thus needs to be on a personalized level. In this paper, we explore historical trip data to compute the driving stress and its impact on various driving behavioral features, captured through vehicle-mounted GPS and inertial sensors. We utilize a Multi-task Learning based Neural Network model to learn both the common features and the personalized features from the driving data to predict the stress level of a driver. We further establish a causal relationship between the stress level of a driver and his driving behavior. Finally, we develop a trip recommendation system for cab drivers to avoid stress driving. The models have been tested over both a publicly available dataset with 6 drivers for 500 minutes of driving data and an in-house collected dataset from 8 drivers over 1700 trips for 5 months. We observe that the proposed model gives an average prediction accuracy of 94% with low false-positive rates. We also observed that the driving behavior is improved when a driver takes a recommended trip.
20 citations
TL;DR: This paper introduces the driving context and models driving behavior in a combination of cognitive perspective and data-driven perspective, and proposes the Predictive-Bi-LSTM-CRF algorithm which used the Bidirectional Long-Short Term Memory Networks and Conditional Random Field as the loss layer.
Abstract: Driving behavior plays a key role in the interaction between vehicle and driver in transportation systems. Some applications about driving behavior in Advanced Driver Assistance Systems (ADAS) improve driving safety significantly. This paper introduces the driving context and models driving behavior in a combination of cognitive perspective and data-driven perspective. First, we use a cognitive fusion method by adding a delay time module to fuse the environmental information and inside information. To better capture the driving context relationship between outside and inside features, we transfer the behavior prediction task to the sequence labeling task by introducing the visual inertia hypothesis. We propose the Predictive-Bi-LSTM-CRF algorithm which used the Bidirectional Long-Short Term Memory Networks (Bi-LSTM) and Conditional Random Field (CRF) as the loss layer to model the driving behavior. Besides, we define a new comprehensive evaluation metric for the prediction task considering F1-score and the prediction time before maneuver together. Our experiment results achieve the state of art performance on the Brain4Cars dataset and demonstrate the applicability of our theory.
14 citations
01 Nov 2019
TL;DR: The neural networks were chosen for the classification task in the proposed for driver decision support system and the proposed methodology utilizes smartphone based sensor data.
Abstract: This paper aims at investigating the usage of smartphone sensor data and machine learning methods to monitor abnormal driver behavior. For this reason, a literature review was carried out in order to get insights into current studies of this field. Different machine learning approaches as well as different sensor data are used and from the findings of the literature. The neural networks were chosen for the classification task in the proposed for driver decision support system. Furthermore, the proposed methodology utilizes smartphone based sensor data. This way the majority of people can access the system no matter their current car.
13 citations
08 Apr 2019
TL;DR: A large-scale driving status and trajectory dataset consisting of 426,992,602 records collected from 68,069 vehicles over a month is presented and an application which recommends the drivers to find the nearby gas stations and possible favorite places from past trips is developed.
Abstract: The increasing number of privately owned vehicles in large metropolitan cities has contributed to traffic congestion, increased energy waste, raised CO2 emissions, and impacted our living conditions negatively. Analysis of data representing citizens' driving behavior can provide insights to reverse these conditions. This article presents a large-scale driving status and trajectory dataset consisting of 426,992,602 records collected from 68,069 vehicles over a month. From the dataset, we analyze the driving behavior and produce random distributions of trip duration and millage to characterize car trips. We have found that a private car has more than 17% probability to make four trips per day, and a trip has more than 25% probability to last 20-30 minutes and 33% probability to travel 10 Kilometers during the trip. The collective distributions of trip mileage and duration follow Weibull distribution, whereas the hourly trips follow the well known diurnal pattern and so the hourly fuel efficiency. Based on these findings, we have developed an application which recommends the drivers to find the nearby gas stations and possible favorite places from past trips. We further highlight that our dataset can be applied for developing dynamic Green maps for fuel-efficient routing, modeling efficient Vehicle-to-Vehicle (V2V) communications, verifying existing V2V protocols, and understanding user behavior in driving their private cars.
12 citations