Proposal of big data route selection methods for autonomous vehicles

2018 ◽  
Vol 1 (5) ◽  
pp. e36 ◽  
Author(s):  
Klaudia Reddig ◽  
Błażej Dikunow ◽  
Karolina Krzykowska
Keyword(s):  
Big Data ◽  
Autonomous Vehicles ◽  
Route Selection ◽  
Selection Methods

Feature selection methods for big data bioinformatics: A survey from the search perspective

Methods ◽  
2016 ◽  
Vol 111 ◽  
pp. 21-31 ◽  
Author(s):  
Lipo Wang ◽  
Yaoli Wang ◽  
Qing Chang
Keyword(s):  
Feature Selection ◽  
Big Data ◽  
Selection Methods

scholarly journals Opportunities and Challenges of Feature Selection Methods for High Dimensional Data: A Review

2021 ◽  
Vol 26 (1) ◽  
pp. 67-77
Author(s):  
Siva Sankari Subbiah ◽  
Jayakumar Chinnappan
Keyword(s):  
Feature Selection ◽  
Big Data ◽  
Large Scale ◽  
High Dimensional Data ◽  
Research Work ◽  
Basic Feature ◽  
High Dimensional ◽  
Selection Methods ◽  
Fast Development ◽  
Improved Accuracy

Now a day, all the organizations collecting huge volume of data without knowing its usefulness. The fast development of Internet helps the organizations to capture data in many different formats through Internet of Things (IoT), social media and from other disparate sources. The dimension of the dataset increases day by day at an extraordinary rate resulting in large scale dataset with high dimensionality. The present paper reviews the opportunities and challenges of feature selection for processing the high dimensional data with reduced complexity and improved accuracy. In the modern big data world the feature selection has a significance in reducing the dimensionality and overfitting of the learning process. Many feature selection methods have been proposed by researchers for obtaining more relevant features especially from the big datasets that helps to provide accurate learning results without degradation in performance. This paper discusses the importance of feature selection, basic feature selection approaches, centralized and distributed big data processing using Hadoop and Spark, challenges of feature selection and provides the summary of the related research work done by various researchers. As a result, the big data analysis with the feature selection improves the accuracy of the learning.

scholarly journals A predictive control for autonomous vehicles using big data analysis

2019 ◽  
Vol 52 (5) ◽  
pp. 191-196 ◽  
Author(s):  
Dániel Fényes ◽  
Balázs Németh ◽  
Péter Gáspar
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Predictive Control ◽  
Autonomous Vehicles ◽  
Big Data Analysis

Big Data Analytics and Network Calculus Enabling Intelligent Management of Autonomous Vehicles in a Smart City

2019 ◽  
Vol 6 (2) ◽  
pp. 2021-2034 ◽  
Author(s):  
Qimei Cui ◽  
Yingze Wang ◽  
Kwang-Cheng Chen ◽  
Wei Ni ◽  
I-Cheng Lin ◽  
...  
Keyword(s):  
Big Data ◽  
Smart City ◽  
Autonomous Vehicles ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Network Calculus ◽  
Intelligent Management

scholarly journals Implementation of a Sensor Big Data Processing System for Autonomous Vehicles in the C-ITS Environment

2020 ◽  
Vol 10 (21) ◽  
pp. 7858
Author(s):  
Aelee Yoo ◽  
Sooyeon Shin ◽  
Junwon Lee ◽  
Changjoo Moon
Keyword(s):  
Big Data ◽  
Autonomous Vehicles ◽  
Processing System ◽  
Intelligent Transport System ◽  
Local Dynamic ◽  
Intelligent Transport ◽  
The Road ◽  
Road Condition ◽  
Dynamic Map ◽  
Using Data

To provide a service that guarantees driver comfort and safety, a platform utilizing connected car big data is required. This study first aims to design and develop such a platform to improve the function of providing vehicle and road condition information of the previously defined central Local Dynamic Map (LDM). Our platform extends the range of connected car big data collection from OBU (On Board Unit) and CAN to camera, LiDAR, and GPS sensors. By using data of vehicles being driven, the range of roads available for analysis can be expanded, and the road condition determination method can be diversified. Herein, the system was designed and implemented based on the Hadoop ecosystem, i.e., Hadoop, Spark, and Kafka, to collect and store connected car big data. We propose a direction of the cooperative intelligent transport system (C-ITS) development by showing a plan to utilize the platform in the C-ITS environment.

Psychophysiological Modeling of Trust In Technology

2021 ◽  
Vol 5 (EICS) ◽  
pp. 1-25
Author(s):  
Ighoyota Ben Ajenaghughrure ◽  
Sonia Cláudia Da Costa Sousa ◽  
David Lamas
Keyword(s):  
Feature Selection ◽  
Risk Perception ◽  
Autonomous Vehicles ◽  
Electrodermal Activity ◽  
Feature Selection Method ◽  
Model Performance ◽  
Selection Method ◽  
Primary Objective ◽  
Selection Methods ◽  
Trust In Technology

Trust as a precursor for users' acceptance of artificial intelligence (AI) technologies that operate as a conceptual extension of humans (e.g., autonomous vehicles (AVs)) is highly influenced by users' risk perception amongst other factors. Prior studies that investigated the interplay between risk and trust perception recommended the development of real-time tools for monitoring cognitive states (e.g., trust). The primary objective of this study was to investigate a feature selection method that yields feature sets that can help develop a highly optimized and stable ensemble trust classifier model. The secondary objective of this study was to investigate how varying levels of risk perception influence users' trust and overall reliance on technology. A within-subject four-condition experiment was implemented with an AV driving game. This experiment involved 25 participants, and their electroencephalogram, electrodermal activity, and facial electromyogram psychophysiological signals were acquired. We applied wrapper, filter, and hybrid feature selection methods on the 82 features extracted from the psychophysiological signals. We trained and tested five voting-based ensemble trust classifier models using training and testing datasets containing only the features identified by the feature selection methods. The results indicate the superiority of the hybrid feature selection method over other methods in terms of model performance. In addition, the self-reported trust measurement and overall reliance of participants on the technology (AV) measured with joystick movements throughout the game reveals that a reduction in risk results in an increase in trust and overall reliance on technology.

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