Threat Assessment Design for Driver Assistance System at Intersections by Georges Aoude, Brandon Luders,
The field of road safety and safe driving has witnessed
rapid advances due to improvements in sensing and computation
technologies. Active safety features like anti-lock
braking systems and adaptive cruise control have been
widely deployed in automobiles to reduce road accidents.
However, the US Department of Transportation (DOT) still
classifies road safety as �a serious and national public health
issue.� In 2008, road accidents in the US caused 37,261
fatalities and about 2.35 million injuries. A particularly
challenging driving task is negotiating a traffic intersection
safely; an estimated 45 percent of injury crashes and 22
percent of roadway fatalities in the US are intersection related. A main contributing factor in these accidents is
the driver�s inability to correctly assess and/or observe the
danger involved in such situations.
This project focuses on assisting human drivers
with negotiating busy intersections in the presence of possibly
errant drivers with uncertain intentions. We are developing a
novel design for a threat assessment module (TAM), which combines
a learning-based intention predictor with an efficient
sampling-based threat assessor to compute the threats of
errant drivers in real-time. This threat data is used to evaluate
the safety of several possible escape paths, which may be
proposed to the human driver if evasive maneuvers are warranted.
The approach is demonstrated through experimental
results in the RAVEN facilities.
The picture below shows
a human-driven (middle-front), and autonomous (acting as possible errant) vehicles near an intersection in the road network of RAVEN. The human-driven vehicle receives warnings from TAM when an errant vehicle detected in its vicinity is predicted to collide with it. A video illustrating the developed approach can be seen here