Material Detail

A key to the development of effective crash countermeasures is an understanding of pre-crash causal and contributing factors. This research effort was initiated to provide an unprecedented level of detail concerning driver performance, behavior, environment, driving context and other factors that were associated with critical incidents, near crashes and crashes for 100 drivers across a period of one year. A primary goal was to provide vital exposure and pre-crash data necessary for understanding causes of crashes, supporting the development and refinement of crash avoidance countermeasures, and estimating the potential of these countermeasures to reduce crashes and their consequences.

The 100-Car Naturalistic Driving Study database contains many extreme cases of driving behavior and performance, including severe fatigue, impairment, judgment error, risk taking, willingness to engage in secondary tasks, aggressive driving, and traffic violations. The data set includes approximately 2,000,000 vehicle miles, almost 43,000 hours of data, 241 primary and secondary drivers, 12 to 13 months of data collection for each vehicle, and data from a highly capable instrumentation system including five channels of video and vehicle kinematics. From the data, an “event” database was created, similar in classification structure to an epidemiological crash database, but with video and electronic driver and vehicle performance data. The events are crashes, near crashes and other “incidents.” Data was classified by pre-event maneuver, precipitating factor, event type, contributing factors, and the avoidance maneuver exhibited. Parameters such as vehicle speed, vehicle headway, time-to-collision, and driver reaction time are also recorded.

This paper presents the 100-Car Naturalistic Driving Study method, including instrumentation and vehicle characteristics, and a sample of study results. Presented analyses address the driver characteristics,

the role of inattention and distraction in rear-end and lane change events. In addition, the methodological attributes of naturalistic data collection and the implications for a larger-scale naturalistic data collection effort are provided.