Title :
Extracting information from continuous naturalistic driving data: sample applications
Author :
Perez, Miguel A. ; Doerzaph, Zachary R. ; Gaylord, Clark K. ; Hankey, Jonathan M.
Author_Institution :
Scientist with the Virginia Tech Transp. Inst., Blacksburg, VA, USA
Abstract :
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.
Keywords :
behavioural sciences computing; driver information systems; information retrieval; road safety; road vehicle radar; RADAR; continuous naturalistic driving data; driver behavior analysis; information extraction; speed data; vehicular crashes; Automotive engineering; Data acquisition; Data engineering; Data mining; Large-scale systems; Radar; Road safety; Road transportation; Vehicle crash testing; Vehicle driving;
Conference_Titel :
Intelligent Vehicles Symposium (IV), 2010 IEEE
Conference_Location :
San Diego, CA
Print_ISBN :
978-1-4244-7866-8
DOI :
10.1109/IVS.2010.5547964