Extracting information from continuous naturalistic driving data: sample applications

The technology and tools used for naturalistic driving data collection have evolved greatly in recent years. Data collection efforts that required a trunk full of equipment and days of installation can now be achieved with data acquisition systems that are about the size of a deck of cards and can b...

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Bibliographic Details
Published in2010 IEEE Intelligent Vehicles Symposium pp. 1201 - 1208
Main Authors Perez, Miguel A., Doerzaph, Zachary R., Gaylord, Clark K., Hankey, Jonathan M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2010
Subjects
Automotive engineering
Data acquisition
Data engineering
Data mining
Large-scale systems
Radar
Road safety
Road transportation
Vehicle crash testing
Vehicle driving
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Summary:The technology and tools used for naturalistic driving data collection have evolved greatly in recent years. Data collection efforts that required a trunk full of equipment and days of installation can now be achieved with data acquisition systems that are about the size of a deck of cards and can be installed in minutes. This evolution has made possible large-scale driving data collection efforts, such as the upcoming Second Safety Highway Research Program Naturalistic Driving Study (SHRP2 NDS). Data from naturalistic studies allow for an unparalleled breadth and depth of driver behavior analysis that goes beyond the quantification and description of driver distraction into a deeper understanding of how drivers interact with their vehicles. This paper describes key aspects of how such studies are designed and executed, and provides some examples of how common types of data are extracted from these naturalistic driving datasets. Specifically, the use of RADAR and speed data are discussed in detail. In addition, a sample architecture for the storage of and access to these vast quantities of driving data and video is provided. Naturalistic driving data have allowed for a transformation in the understanding of driver behavior and, as datasets are expanded to include diverse populations, they will help researchers and automotive engineers in developing novel ways to mitigate and prevent vehicular crashes and their consequences.
ISBN:1424478669
9781424478668
ISSN:1931-0587
2642-7214
DOI:10.1109/IVS.2010.5547964