NHTSA is conducting research to evaluate the current state-of-the-art technology
for lane departure warning (LDW) and lane-keeping assistance (LKA) technology.
NHTSA is undertaking research to understand the nature of real-world lane
departures and recovery behaviors. While some information about lane departures
can be learned from crash datasets, the purpose of this work was to mine
simulator datasets for lane departures, analyze them in greater detail than is
possible from crash reports or naturalistic studies, and link their
characteristics to driver drowsiness. The objective of the study was to
determine whether there are differences in lane departure characteristics as a
function of driver drowsiness. This research used a novel approach by combining
data from six different driving simulator studies on driver drowsiness. The
dataset included a sample of 380 drivers. Study drives occurred during overnight
hours after periods of sleep deprivation, with participants being awake for at
least 16 h prior to driving. Study drives ranged in duration from relatively
short 45-min to nearly 4 h. The datasets were reduced to characterize 5805
individual lane departures. Lane departures were delineated into three phases
(pre-departure, departure, and recovery) and two transition points (onset and
reentry) to capture driver behaviors under drowsiness. We hypothesized that lane
departures would look different under different levels of drowsiness. Drives
took place across a range of roadway environments that included interstate
highways, rural highways, rural roads, and low-speed urban areas. Drowsiness was
sampled at points before, during, and after the drive using self-ratings
[Karolinska Sleepiness Scale (KSS) or Stanford Sleepiness Scale (SSS)] as well
as the expert Observational Rating of Drowsiness (ORD). High levels of
drowsiness were associated with a narrow speed range at highway speeds and the
least amount of throttle input, while low levels of drowsiness had more steering
activity, more throttle input, and a broader range of speeds. The results of
this study will improve understanding of vehicle kinematics and driver behavior
in drowsy lane departures using a safe methodology to help address crash dataset
limitations.
