Objective, detailed and accurate data is critically important in the effort to determine the causes and contributing factors of crashes. In the past, the only way to obtain such information for a large number of crashes was to use data collected from police reports.
While information gathered this way is helpful, it has many limitations. More recently, invehicle event recorders (IVERs) have become a widely accepted means of gathering crash data, both in research and real-world applications.
In this study, we conducted the first-ever large-scale examination of naturalistic crash data. Other naturalistic studies have investigated only a small number of crashes or used near crashes as a proxy for real crashes. In contrast, this project examined hundreds of actual crashes from a naturalistic driving database. The data allowed us to examine behaviors and potential contributing factors in the seconds leading up to the collision, and provided information not available in police reports.
A coding scheme was developed specifically for this study, and video data were coded with the goal of identifying the factors that contributed to crashes—in particular the prevalence of potentially distracting driver behaviors and drowsiness. The study addressed the following research questions:
- What were the roadway and environmental conditions at the time of the crash?
- What were the critical events and potential contributing factors leading up to the crash and did these differ by crash type?
- What driver behaviors were present in the vehicle prior to the crash and did these differ by crash type?
- How did driver response times and eyes-off-road time differ relative to certain driver behaviors and crash types?
- Could drowsy driving be detected using this type of crash data?
Understanding the prevalence of factors that potentially contribute to crashes will provide a significant societal benefit and advance the field of traffic and crash safety. More specifically, information regarding what is happening inside the vehicle during the seconds before a crash can be used to pinpoint automotive safety systems and technologies that might best mitigate certain types of crashes.