An interactive data-driven driving simulator using motion blending

2008 ◽  
Vol 59 (5) ◽  
pp. 520-531 ◽  
Author(s):  
Moohyun Cha ◽  
Jeongsam Yang ◽  
Soonhung Han
Keyword(s):  
Driving Simulator ◽  
Data Driven ◽  
Interactive Data

scholarly journals Interactive Data Driven Visualization for COVID-19 with Trends, Analytics and Forecasting

2020 ◽  
Author(s):  
Frincy Clement ◽  
Asket Kaur ◽  
Maryam Sedghi ◽  
Deepa Krishnaswamy ◽  
Kumaradevan Punithakumar
Keyword(s):  
Data Driven ◽  
Interactive Data

An Interactive Data-Driven HPC System for Forecasting Weather, Wildland Fire, and Smoke

2019 ◽  
Author(s):  
Jan Mandel ◽  
Martin Vejmelka ◽  
Adam Kochanski ◽  
Angel Farguell ◽  
James Haley ◽  
...  
Keyword(s):  
Wildland Fire ◽  
Data Driven ◽  
Interactive Data

scholarly journals Citizen Science and Topology of Mind: Complexity, Computation and Criticality in Data-Driven Exploration of Open Complex Systems

2017 ◽  
Author(s):  
Masatoshi Funabashi
Keyword(s):  
Complex Systems ◽  
Citizen Science ◽  
Model Development ◽  
Data Driven ◽  
Massive Data ◽  
Theoretical Frameworks ◽  
Complexity Measures ◽  
And Topology ◽  
Interactive Data

Recently emerging data-driven citizen sciences need to harness increasing amount of massive data with varying quality. This paper develops essential theoretical frameworks and example models and examine its computational complexity for interactive data-driven citizen science within the context of guided self-organization. We first define a conceptual model that incorporates quality of observation in terms of accuracy and reproducibility, ranging between subjectivity, inter-subjectivity, and objectivity. Next, we examine the database's algebraic and topological structure in relation to informational complexity measures, and evaluate its computational complexities with respect to exhaustive optimization. Conjectures of criticality are obtained on self-organizing processes of observation and dynamical model development. Example analysis is demonstrated with the use of biodiversity assessment database, the process that inevitably involves human subjectivity for the management in open complex systems.

scholarly journals How to create an interactive dashboard using R: the example of the Queensland COVID-19 tracker

2020 ◽  
Vol 4 (2) ◽  
pp. 39-47
Author(s):  
Julia Loginova ◽  
Pia Wohland
Keyword(s):  
Low Cost ◽  
Data Driven ◽  
Web Page ◽  
Software Environment ◽  
Online Data ◽  
Interactive Tools ◽  
Queensland Health ◽  
Dissemination Of Research ◽  
Supplementary Material ◽  
Interactive Data

Background  Interactive tools like data dashboards enable users both to view and interact with data. In today’s data-driven environment it is a priority for researchers and practitioners alike to be able to develop interactive data visualisation tools easily and where possible at a low cost. Aims  Here, we provide a guide on how to develop and create an interactive online data dashboard in R, using the COVID-19 tracker for Health and Hospital Regions in Queensland, Australia as an example. We detail a series of steps and explain choices made to design, develop, and easily maintain the dashboard and publish it online. Data and methods  The dashboard visualises publicly available data from the Queensland Health web page. We used the programming language R and its free software environment. The dashboard webpage is hosted publicly on GitHub Pages updated via GitHub Desktop. Results  Our interactive dashboard is available at https://qcpr.github.io/. Conclusions  Interactive dashboards have many applications such as dissemination of research and other data. This guide and the supplementary material can be adjusted to develop a new dashboard for a different set of data and needs.

scholarly journals A Recognition Model of Driving Risk Based on Belief Rule-Base Methodology

2018 ◽  
Vol 32 (11) ◽  
pp. 1850037 ◽  
Author(s):  
Chuan Sun ◽  
Chaozhong Wu ◽  
Duanfeng Chu ◽  
Zhenji Lu ◽  
Jian Tan ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Data Driven ◽  
Driving Safety ◽  
Rule Base ◽  
Driver Assistance Systems ◽  
Recognition Model ◽  
Time Operation ◽  
Belief Rule Base ◽  
Driving Risk ◽  
Data Driven Modeling

This paper aims to recognize driving risks in individual vehicles online based on a data-driven methodology. Existing advanced driver assistance systems (ADAS) have difficulties in effectively processing multi-source heterogeneous driving data. Furthermore, parameters adopted for evaluating the driving risk are limited in these systems. The approach of data-driven modeling is investigated in this study for utilizing the accumulation of on-road driving data. A recognition model of driving risk based on belief rule-base (BRB) methodology is built, predicting driving safety as a function of driver characteristics, vehicle state and road environment conditions. The BRB model was calibrated and validated using on-road data from 30 drivers. The test results show that the recognition accuracy of our proposed model can reach about 90% in all situations with three levels (none, medium, large) of driving risks. Furthermore, the proposed simplified model, which provides real-time operation, is implemented in a vehicle driving simulator as a reference for future ADAS and belongs to research on artificial intelligence (AI) in the automotive field.

scholarly journals Data-driven vehicle speed detection from synthetic driving simulator images

2021 ◽  
Author(s):  
A. Hernandez Martinez ◽  
J. Lorenzo Diaz ◽  
I. Garcia Daza ◽  
D. Fernandez Llorca
Keyword(s):  
Driving Simulator ◽  
Data Driven ◽  
Vehicle Speed

Interactive Data-Driven Process Model Construction

2018 ◽  
pp. 251-265 ◽  
Author(s):  
P. M. Dixit ◽  
H. M. W. Verbeek ◽  
J. C. A. M. Buijs ◽  
W. M. P. van der Aalst
Keyword(s):  
Process Model ◽  
Model Construction ◽  
Data Driven ◽  
Interactive Data
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