scholarly journals An Intelligent Task Scheduling Mechanism for Autonomous Vehicles via Deep Learning

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1788
Author(s):  
Gomatheeshwari Balasekaran ◽  
Selvakumar Jayakumar ◽  
Rocío Pérez de Prado
Keyword(s):  
Execution Time ◽  
Autonomous Vehicles ◽  
Rapid Development ◽  
Single Layer ◽  
Battery Life ◽  
Learning Approaches ◽  
Task Management ◽  
Inference System ◽  
Safe Driving ◽  
Self Driving Cars

With the rapid development of the Internet of Things (IoT) and artificial intelligence, autonomous vehicles have received much attention in recent years. Safe driving is one of the essential concerns of self-driving cars. The main problem in providing better safe driving requires an efficient inference system for real-time task management and autonomous control. Due to limited battery life and computing power, reducing execution time and resource consumption can be a daunting process. This paper addressed these challenges and developed an intelligent task management system for IoT-based autonomous vehicles. For each task processing, a supervised resource predictor is invoked for optimal hardware cluster selection. Tasks are executed based on the earliest hyper period first (EHF) scheduler to achieve optimal task error rate and schedule length performance. The single-layer feedforward neural network (SLFN) and lightweight learning approaches are designed to distribute each task to the appropriate processor based on their emergency and CPU utilization. We developed this intelligent task management module in python and experimentally tested it on multicore SoCs (Odroid Xu4 and NVIDIA Jetson embedded platforms).Connected Autonomous Vehicles (CAV) and Internet of Medical Things (IoMT) benchmarks are used for training and testing purposes. The proposed modules are validated by observing the task miss rate, resource utilization, and energy consumption metrics compared with state-of-art heuristics. SLFN-EHF task scheduler achieved better results in an average of 98% accuracy, and in an average of 20–27% reduced in execution time and 32–45% in task miss rate metric than conventional methods.

Cloning Safe Driving Behavior for Self-Driving Cars using Convolutional Neural Networks

2019 ◽  
Vol 12 (2) ◽  
pp. 120-127 ◽  
Author(s):  
Wael Farag
Keyword(s):  
Gradient Descent ◽  
Autonomous Driving ◽  
Driving Behavior ◽  
Training Data ◽  
Stochastic Gradient Descent ◽  
Data Set ◽  
Safe Driving ◽  
Processing Pipeline ◽  
Self Driving Cars ◽  
And Training

Background: In this paper, a Convolutional Neural Network (CNN) to learn safe driving behavior and smooth steering manoeuvring, is proposed as an empowerment of autonomous driving technologies. The training data is collected from a front-facing camera and the steering commands issued by an experienced driver driving in traffic as well as urban roads. Methods: This data is then used to train the proposed CNN to facilitate what it is called “Behavioral Cloning”. The proposed Behavior Cloning CNN is named as “BCNet”, and its deep seventeen-layer architecture has been selected after extensive trials. The BCNet got trained using Adam’s optimization algorithm as a variant of the Stochastic Gradient Descent (SGD) technique. Results: The paper goes through the development and training process in details and shows the image processing pipeline harnessed in the development. Conclusion: The proposed approach proved successful in cloning the driving behavior embedded in the training data set after extensive simulations.

The ideological divide in public perceptions of self-driving cars

2020 ◽  
Vol 29 (4) ◽  
pp. 436-451
Author(s):  
Yilang Peng
Keyword(s):  
Artificial Intelligence ◽  
United States ◽  
Scientific Literacy ◽  
Autonomous Vehicles ◽  
The United States ◽  
Public Perceptions ◽  
New Information ◽  
Nationally Representative ◽  
Self Driving Cars ◽  
Social Conservatism

Applications in artificial intelligence such as self-driving cars may profoundly transform our society, yet emerging technologies are frequently faced with suspicion or even hostility. Meanwhile, public opinions about scientific issues are increasingly polarized along the ideological line. By analyzing a nationally representative panel in the United States, we reveal an emerging ideological divide in public reactions to self-driving cars. Compared with liberals and Democrats, conservatives and Republicans express more concern about autonomous vehicles and more support for restrictively regulating autonomous vehicles. This ideological gap is largely driven by social conservatism. Moreover, both familiarity with driverless vehicles and scientific literacy reduce respondents’ concerns over driverless vehicles and support for regulation policies. Still, the effects of familiarity and scientific literacy are weaker among social conservatives, indicating that people may assimilate new information in a biased manner that promotes their worldviews.

A Comparison of Presentation Mediums for the Study of Trust in Autonomous Vehicles

2021 ◽  
Vol 65 (1) ◽  
pp. 878-882
Author(s):  
Katherine Garcia ◽  
Ian Robertson ◽  
Philip Kortum
Keyword(s):  
Autonomous Vehicles ◽  
Automated System ◽  
Spatial Presence ◽  
Viewing Condition ◽  
Stop Sign ◽  
Flat Screen ◽  
The Mean ◽  
Presentation Methods ◽  
Self Driving Cars ◽  
User Trust

The purpose of this study is to compare presentation methods for use in the validation of the Trust in Selfdriving Vehicle Scale (TSDV), a questionnaire designed to assess user trust in self-driving cars. Previous studies have validated trust instruments using traditional videos wherein participants watch a scenario involving an automated system but there are strong concerns about external validity with this approach. We examined four presentation conditions: a flat screen monitor with a traditional video, a flat screen with a 2D 180 video, an Oculus Go VR headset with a 2D 180 video, and an Oculus Go with a 3D VR video. Participants watched eight video scenarios of a self-driving vehicle attempting a right-hand tum at a stop sign and rated their trust in the vehicle shown in the video after each scenario using the TSDV and rated telepresence for the viewing condition. We found a significant interaction between the mean TSDV scores for pedestrian collision and presentation condition. The TSDV mean in the Headset 2D 180 condition was significantly higher than the other three conditions. Additionally, when used to view the scenarios as 3D VR videos, the headset received significantly higher ratings of spatial presence compared to the condition using a flatscreen a 2D video; none of the remaining comparisons were statistically significant. Based on the results it is not recommended that the headset be used for short scenarios because the benefits do not outweigh the costs.

scholarly journals A Heuristic Evolutionary Algorithm of UAV Path Planning

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Zhangjie Fu ◽  
Jingnan Yu ◽  
Guowu Xie ◽  
Yiming Chen ◽  
Yuanhang Mao
Keyword(s):  
Energy Consumption ◽  
Evolutionary Algorithm ◽  
Rapid Development ◽  
Optimal Path ◽  
Battery Life ◽  
Intelligent Robot ◽  
Urgent Problem ◽  
Intelligent Robots ◽  
Aerial Vehicle ◽  
The Cost

With the rapid development of the network and the informatization of society, how to improve the accuracy of information is an urgent problem to be solved. The existing method is to use an intelligent robot to carry sensors to collect data and transmit the data to the server in real time. Many intelligent robots have emerged in life; the UAV (unmanned aerial vehicle) is one of them. With the popularization of UAV applications, the security of UAV has also been exposed. In addition to some human factors, there is a major factor in the UAV’s endurance. UAVs will face a problem of short battery life when performing flying missions. In order to solve this problem, the existing method is to plan the path of UAV flight. In order to find the optimal path for a UAV flight, we propose three cost functions: path security cost, length cost, and smoothness cost. The path security cost is used to determine whether the path is feasible; the length cost and smoothness cost of the path directly affect the cost of the energy consumption of the UAV flight. We proposed a heuristic evolutionary algorithm that designed several evolutionary operations: substitution operations, crossover operations, mutation operations, length operations, and smoothness operations. Through these operations to enhance our build path effect. Under the analysis of experimental results, we proved that our solution is feasible.

Pedestrians, Autonomous Vehicles, and Cities

2016 ◽  
Vol 38 (1) ◽  
pp. 6-12 ◽  
Author(s):  
Adam Millard-Ball
Keyword(s):  
Travel Behavior ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Urban Neighborhoods ◽  
Urban Traffic ◽  
Risk Averse ◽  
Strategic Interactions ◽  
Road Users ◽  
Self Driving Cars ◽  
Time Autonomous

Autonomous vehicles, popularly known as self-driving cars, have the potential to transform travel behavior. However, existing analyses have ignored strategic interactions with other road users. In this article, I use game theory to analyze the interactions between pedestrians and autonomous vehicles, with a focus on yielding at crosswalks. Because autonomous vehicles will be risk-averse, the model suggests that pedestrians will be able to behave with impunity, and autonomous vehicles may facilitate a shift toward pedestrian-oriented urban neighborhoods. At the same time, autonomous vehicle adoption may be hampered by their strategic disadvantage that slows them down in urban traffic.

scholarly journals FedPARL: Client Activity and Resource-Oriented Lightweight Federated Learning Model for Resource-Constrained Heterogeneous IoT Environment

2021 ◽  
Vol 2 ◽  
Author(s):  
Ahmed Imteaj ◽  
M. Hadi Amini
Keyword(s):  
Machine Learning ◽  
Resource Availability ◽  
Resource Constraints ◽  
Training Model ◽  
Convergence Time ◽  
Battery Life ◽  
Sensitive Information ◽  
Learning Approaches ◽  
Resource Constrained ◽  
Distributed Machine Learning

Federated Learning (FL) is a recently invented distributed machine learning technique that allows available network clients to perform model training at the edge, rather than sharing it with a centralized server. Unlike conventional distributed machine learning approaches, the hallmark feature of FL is to allow performing local computation and model generation on the client side, ultimately protecting sensitive information. Most of the existing FL approaches assume that each FL client has sufficient computational resources and can accomplish a given task without facing any resource-related issues. However, if we consider FL for a heterogeneous Internet of Things (IoT) environment, a major portion of the FL clients may face low resource availability (e.g., lower computational power, limited bandwidth, and battery life). Consequently, the resource-constrained FL clients may give a very slow response, or may be unable to execute expected number of local iterations. Further, any FL client can inject inappropriate model during a training phase that can prolong convergence time and waste resources of all the network clients. In this paper, we propose a novel tri-layer FL scheme, Federated Proximal, Activity and Resource-Aware 31 Lightweight model (FedPARL), that reduces model size by performing sample-based pruning, avoids misbehaved clients by examining their trust score, and allows partial amount of work by considering their resource-availability. The pruning mechanism is particularly useful while dealing with resource-constrained FL-based IoT (FL-IoT) clients. In this scenario, the lightweight training model will consume less amount of resources to accomplish a target convergence. We evaluate each interested client's resource-availability before assigning a task, monitor their activities, and update their trust scores based on their previous performance. To tackle system and statistical heterogeneities, we adapt a re-parameterization and generalization of the current state-of-the-art Federated Averaging (FedAvg) algorithm. The modification of FedAvg algorithm allows clients to perform variable or partial amounts of work considering their resource-constraints. We demonstrate that simultaneously adapting the coupling of pruning, resource and activity awareness, and re-parameterization of FedAvg algorithm leads to more robust convergence of FL in IoT environment.

scholarly journals What drives you to use a driverless car?

2020 ◽  
Author(s):  
Sándor Huszár ◽  
Zoltán Majó-Petri
Keyword(s):  
Technology Acceptance ◽  
Influencing Factors ◽  
Behavioral Intention ◽  
Autonomous Vehicles ◽  
Structural Equation ◽  
Technological Development ◽  
Perceived Safety ◽  
Psychological Models ◽  
Acceptance Model ◽  
Self Driving Cars

The investigation of driverless car from the economic perspective is one of the most discussed topics nowadays. Although it can be approached from various perspectives there is still a lack of studies focusing on the behavioral intention to use self-driving cars and its influencing factors. Over the last few decades, various psychological models have been developed to investigate the influencing factors of usage of certain technologies, but most of them cannot provide clear answers on consumer attitudes and intentions with regard to autonomous vehicles. Thus, new models have appeared to better describe the psychological factors of this new technological development that will revolutionize the future of mobility. In our research CTAM (Car Technology Acceptance Model) was used to measure intention to using self-driving cars. In 2019, 314 participants responded to our questionnaire and provided answers to the given questions. We used structural equation modelling to investigate the linkages between the behavioral intention and influencing factors revealed during the literature review. According to the results, the most important influencing factors of intention are attitude, perceived safety and social norms, while anxiety (of using the technology), effort expectancy, performance expectancy, and self-efficacy have not been proven important factors. The model used in our investigation explains behavioral intention to a great extent (63%).

scholarly journals Graph Learning based Recommender Systems: A Review

2021 ◽  
Author(s):  
Shoujin Wang ◽  
Liang Hu ◽  
Yan Wang ◽  
Xiangnan He ◽  
Quan Z. Sheng ◽  
...  
Keyword(s):  
Systematic Review ◽  
Recommender Systems ◽  
Rapid Development ◽  
Research Area ◽  
Learning Approaches ◽  
Graph Learning ◽  
Promising Direction ◽  
Learning Techniques ◽  
Fast Development ◽  
Content Based Filtering

Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS). GLRS mainly employ advanced graph learning approaches to model users’ preferences and intentions as well as items’ characteristics and popularity for Recommender Systems (RS). Differently from other approaches, including content based filtering and collaborative filtering, GLRS are built on graphs where the important objects, e.g., users, items, and attributes, are either explicitly or implicitly connected. With the rapid development of graph learning techniques, exploring and exploiting homogeneous or heterogeneous relations in graphs is a promising direction for building more effective RS. In this paper, we provide a systematic review of GLRS, by discussing how they extract knowledge from graphs to improve the accuracy, reliability and explainability of the recommendations. First, we characterize and formalize GLRS, and then summarize and categorize the key challenges and main progress in this novel research area.

scholarly journals Peran Kepemimpinan Kiai di Pendidikan Pesantren dalam Menghadapi Revolusi Industri 4.0

2020 ◽  
Vol 11 (1) ◽  
pp. 21-34
Author(s):  
Sulaiman Sulaiman ◽  
Asnawan Asnawan
Keyword(s):  
Information Technology ◽  
Industrial Revolution ◽  
Technology Development ◽  
Rapid Development ◽  
National Character ◽  
Learning Approaches ◽  
Religious Knowledge ◽  
Information Technology Development ◽  
The Times

This paper discusses the role of the kiai's leadership in pesantren education in an effort to strengthen the Industrial Revolution 4.0 industry. Pesantren is an ideal education system that attempts to provide education to the community to develop the existing potential, because pesantren santri in pesantren are not only given religious knowledge, santri are also taught to live independently, have character and innovate through pesantren activities. The pesantren is one of the institutions of Islamic education that will produce a generation of Indonesians into 'ulama', Muslim scholars and generations who have national character and morality. In the current era of information technology development, learning approaches have experienced rapid development that can change people's mindsets. The availability of information technology that is connected to the internet makes it easy for everyone to access science. So that pesantren must also be able to actualize these developments by developing pesantren curricula that are in accordance with the needs of the times. Then what is the role of pesantren in facing the development of globalization, what strategies are suitable in dealing with generations of the millennial era.

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