Real-World Connected Vehicle Data, Deep Learning-based Sensing Technologies, and Decision-Making Self-Driving Car Control Algorithms in Autonomous Mobility Systems

2021 ◽  
Vol 13 (1) ◽  
pp. 81
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Real World ◽  
Control Algorithms ◽  
Connected Vehicle ◽  
Vehicle Data ◽  
Autonomous Mobility

scholarly journals Review of Usage of Real-World Connected Vehicle Data

2020 ◽  
Vol 2674 (10) ◽  
pp. 939-950
Author(s):  
Yun Zhou ◽  
Raj Bridgelall
Keyword(s):  
Real World ◽  
Large Scale ◽  
The United States ◽  
Connected Vehicles ◽  
United States Department ◽  
Connected Vehicle ◽  
Pilot Model ◽  
Vehicle Data ◽  
World Information ◽  
Vehicle Technologies

GPS loggers and cameras aboard connected vehicles can produce vast amounts of data. Analysts can mine such data to decipher patterns in vehicle trajectories and driver–vehicle interactions. Ability to process such large-scale data in real time can inform strategies to reduce crashes, improve traffic flow, enhance system operational efficiencies, and reduce environmental impacts. However, connected vehicle technologies are in the very early phases of deployment. Therefore, related datasets are extremely scarce, and the utility of such emerging datasets is largely unknown. This paper provides a comprehensive review of studies that used large-scale connected vehicle data from the United States Department of Transportation Connected Vehicle Safety Pilot Model Deployment program. It is the first and only such dataset available to the public. The data contains real-world information about the operation of connected vehicles that organizations are testing. The paper provides a summary of the available datasets and their organization, and the overall structure and other characteristics of the data captured during pilot deployments. Usage of the data is then classified into three categories: driving pattern identification, development of surrogate safety measures, and improvements in the operation of signalized intersections. Finally, some limitations experienced with the existing datasets are identified.

Applying Deep Learning to Audit Procedures: An Illustrative Framework

2019 ◽  
Vol 33 (3) ◽  
pp. 89-109 ◽  
Author(s):  
Ting (Sophia) Sun
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Data Warehouse ◽  
Visual Recognition ◽  
Prediction Models ◽  
Historical Data ◽  
Structured Data ◽  
Text Understanding ◽  
Audit Data ◽  
Learning Functions

SYNOPSIS This paper aims to promote the application of deep learning to audit procedures by illustrating how the capabilities of deep learning for text understanding, speech recognition, visual recognition, and structured data analysis fit into the audit environment. Based on these four capabilities, deep learning serves two major functions in supporting audit decision making: information identification and judgment support. The paper proposes a framework for applying these two deep learning functions to a variety of audit procedures in different audit phases. An audit data warehouse of historical data can be used to construct prediction models, providing suggested actions for various audit procedures. The data warehouse will be updated and enriched with new data instances through the application of deep learning and a human auditor's corrections. Finally, the paper discusses the challenges faced by the accounting profession, regulators, and educators when it comes to applying deep learning.

scholarly journals Movie Recommendation through Multiple Bias Analysis

2021 ◽  
Vol 11 (6) ◽  
pp. 2817
Author(s):  
Tae-Gyu Hwang ◽  
Sung Kwon Kim
Keyword(s):  
Decision Making ◽  
Recommender System ◽  
Real World ◽  
Matrix Factorization ◽  
User Preferences ◽  
Improve Performance ◽  
Bias Analysis ◽  
Useful Knowledge ◽  
Existing Problems ◽  
Movie Recommendation

A recommender system (RS) refers to an agent that recommends items that are suitable for users, and it is implemented through collaborative filtering (CF). CF has a limitation in improving the accuracy of recommendations based on matrix factorization (MF). Therefore, a new method is required for analyzing preference patterns, which could not be derived by existing studies. This study aimed at solving the existing problems through bias analysis. By analyzing users’ and items’ biases of user preferences, the bias-based predictor (BBP) was developed and shown to outperform memory-based CF. In this paper, in order to enhance BBP, multiple bias analysis (MBA) was proposed to efficiently reflect the decision-making in real world. The experimental results using movie data revealed that MBA enhanced BBP accuracy, and that the hybrid models outperformed MF and SVD++. Based on this result, MBA is expected to improve performance when used as a system in related studies and provide useful knowledge in any areas that need features that can represent users.

scholarly journals Explaining deep neural networks for knowledge discovery in electrocardiogram analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Steven A. Hicks ◽  
Jonas L. Isaksen ◽  
Vajira Thambawita ◽  
Jonas Ghouse ◽  
Gustav Ahlberg ◽  
...  
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Clinical Decision Making ◽  
Medical Knowledge ◽  
Clinical Decision ◽  
Medical Data ◽  
Medical Doctors ◽  
Medical Tests ◽  
Deep Learning Model

AbstractDeep learning-based tools may annotate and interpret medical data more quickly, consistently, and accurately than medical doctors. However, as medical doctors are ultimately responsible for clinical decision-making, any deep learning-based prediction should be accompanied by an explanation that a human can understand. We present an approach called electrocardiogram gradient class activation map (ECGradCAM), which is used to generate attention maps and explain the reasoning behind deep learning-based decision-making in ECG analysis. Attention maps may be used in the clinic to aid diagnosis, discover new medical knowledge, and identify novel features and characteristics of medical tests. In this paper, we showcase how ECGradCAM attention maps can unmask how a novel deep learning model measures both amplitudes and intervals in 12-lead electrocardiograms, and we show an example of how attention maps may be used to develop novel ECG features.

Real‐world evidence to support regulatory decision making: New or expanded medical product indications

2021 ◽  
Author(s):  
Jessica M. Franklin ◽  
Kai‐Li Liaw ◽  
Solomon Iyasu ◽  
Cathy Critchlow ◽  
Nancy Dreyer
Keyword(s):  
Decision Making ◽  
Real World ◽  
Medical Product ◽  
Regulatory Decision ◽  
Real World Evidence

Artificial Intelligence and Human Rights: Four Realms of Discussion: Summary of Remarks

2020 ◽  
Vol 114 ◽  
pp. 242-245
Author(s):  
Jootaek Lee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Language Processing ◽  
Human Reasoning ◽  
The Past ◽  
Making Choices ◽  
Data Capturing ◽  
Intensive Procedures

The term, Artificial Intelligence (AI), has changed since it was first coined by John MacCarthy in 1956. AI, believed to have been created with Kurt Gödel's unprovable computational statements in 1931, is now called deep learning or machine learning. AI is defined as a computer machine with the ability to make predictions about the future and solve complex tasks, using algorithms. The AI algorithms are enhanced and become effective with big data capturing the present and the past while still necessarily reflecting human biases into models and equations. AI is also capable of making choices like humans, mirroring human reasoning. AI can help robots to efficiently repeat the same labor intensive procedures in factories and can analyze historic and present data efficiently through deep learning, natural language processing, and anomaly detection. Thus, AI covers a spectrum of augmented intelligence relating to prediction, autonomous intelligence relating to decision making, automated intelligence for labor robots, and assisted intelligence for data analysis.

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