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Summary & Details

IMPLEMENTING COMPREHENSIVE CLOUD-BASED PLATFORMS FOR NATURALISTIC DRIVING STUDIES

Delivery: Available on this site

Format: PDF

Price: Non-members (tax incl.)：￥1,100 Members (tax incl.)：￥880

Publication code: 20219010

Paper/Info type: Other International Conferences

Pages: 1-6(Total 6 p)

Date of publication: Sep 2021

Publisher: JSAE

Language: English

Detailed Information

Author(E)	1) Archana Venkatachalapathy, 2) Aditya Raj, 3) Jennifer Merickel, 4) Anuj Sharma
Affiliation(E)	1) Iowa State University, 2) Iowa State University, 3) University of Nebraska Medical Center, 4) Iowa State University
Abstract(E)	Naturalistic driving studies are a sought-after method to research driving behavior. In a Naturalistic Driving Study, drivers are requested to drive as they typically would while sensors installed in their vehicle collect large amounts of detailed data on driving patterns (e.g., speed, acceleration, throttle, GPS) that can be merged with roadway, weather, lighting, and other databases. Given the large amount of complex data collected, there is a need for robust data systems for storing, mining, visualizing, and analyzing this big naturalistic data. We designed a comprehensive cloud-based AI platform (Deep Insight) for data management, modeling, and enhanced annotations for naturalistic driving data to address this need. The platform capitalizes on Amazon Web Services, hosting a repository of public and privately collected NDS datasets with tool integration for data annotation and machine learning modeling that permits data analysis and inference. This end-to-end framework provides effective and reliable tools for storing, processing, annotating, modeling, and visualizing NDS datasets. Critically, it permits a partially automated analysis pipeline that reduces the manual labor and time required to process and model large datasets. Integration with RocketML and dockers allows users to train and deploy machine learning models efficiently without dependency on the computing environment. This cloud-based platform offers a wide range of benefits in terms of cost, access, scalability, and security, aiming to create a one-stop destination for analyzing naturalistic driving data and studying driver behavior.

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