Roadway departures remain a major cause of crashes, injuries, and fatalities on
U.S. roads. Technologies such as lane keeping assist (LKA) and lane centering
assist (LCA) can help mitigate these crashes, but their development involves
extensive characterization of the parameter space in which they operate. Lane
and road departures (LDs/RDs) and lane changes (LCs) must be systematically
described and quantified to distinguish kinematic features, identify
contributing factors, and benchmark system influence on lateral control.
This study developed a unified pipeline to mine over 36 million miles of
naturalistic driving study (NDS) data collected from more than 3800
participants. The pipeline integrates various types of signals to detect roadway
boundary crossings, classify LKA-relevant scenarios, and extract roadway,
driver, environmental, and assistance-related parameters. Lane keeping epochs
with and without LKA were also extracted to quantify system influence on lateral
control.
In the NDS analysis, crashes include both object contact events and RDs, defined
as non-premeditated departures from the intended travel surface involving at
least one tire. Analysis of pre-identified crashes in the NDS showed that
unintentional RDs accounted for 5.67%, unintentional LDs for 1.76%, and
intentional LCs for 1.55%, corresponding to lower-bound rates of 2.7, 0.8, and
0.7 crashes per million vehicle miles traveled. RD crashes were predominantly
right-sided, LD crashes left-sided, and both were overrepresented on curves and
under adverse conditions. Loss of control preceded 22% of RD crashes and 69% of
LD crashes.
Beyond crashes and near-crashes (CNCs), the algorithm identified approximately 3
million LCs and 0.3 million LDs/RDs. LCs typically involved larger crossing
angles that decreased with speed, while departures clustered within 0°–2°.
Compared with CNCs, these occurred at higher speeds and smaller angles. LKA
consistently reduced lateral variability without biasing the mean offset.
