Title
Predictive Modeling with Vehicle Sensor Data and IoT for Injury Prevention
Abstract
Automobile accidents remain one of the leading causes of death in the United States. Sensor-based driver assistance systems have made driving safer by lending drivers an extra pair of eyes and an information triage center. The Internet of Things technologies enable information exchange between drivers, vehicles, and roads leading towards intelligent transportation systems. Efforts to further injury prevention in the past decade have been focused on heterogenous information sourcing and predictive analytics on driver intent. The federal naturalistic driving database Strategic Highway Research Program 2 (SHRP 2) is unprecedented in that it provides a wealth of data resulted from real-time sensor capture of in-progress driving trips by a large cohort. We discuss our novel approach to study injury risk factors using temporal heterogenous network mining and address the challenge of algorithmic efficiency associated with large datasets by leveraging distributed computing modules.
Year
DOI
Venue
2018
10.1109/CIC.2018.00047
2018 IEEE 4th International Conference on Collaboration and Internet Computing (CIC)
Keywords
Field
DocType
predictive modeling,injury prevention,IoT,sensor analytics,SHRP 2 data,temporal heterogeneous network mining
Data science,Research program,Computer vision,Computer science,Predictive analytics,Information exchange,Advanced driver assistance systems,SAFER,Injury prevention,Artificial intelligence,Intelligent transportation system,TRIPS architecture
Conference
ISBN
Citations 
PageRank 
978-1-5386-9503-6
0
0.34
References 
Authors
8
6
Name
Order
Citations
PageRank
Christopher C. Yang11590138.09
Ou Liang200.34
Santiago Ontañón361978.32
Weimao Ke429623.27
Helen Loeb500.34
Charlie Klauer600.34