Driver Adaptive Warning Systems
Abstract
Each year, many preventable highway automobile accidents involving
single vehicles are caused by inattention and distraction. These
accidents are classified as single vehicle road departures. Lane
departure and curve negotiation warning systems are an emerging
technology to help prevent these types of accidents. I plan to build a
road departure warning system that learns individual driver behavior,
and uses this knowledge to reduce false alarms and increase warning
time. Current warning systems are physics based -- they look at
vehicle trajectory, but mainly ignore driver ability and
characteristics. I propose to develop an adaptive lane departure and
curve negotiation warning sys tem. This system should learn individual
traits of the driver -- both stationary and changing, and use this
information to improve warning time and reduce false alarms. A number
of research issues are involved in this work, as it has to improve
upon the state of the art, yet not become so complicated to use that
the average driver would feel uncomfortable using it. In this
proposal, I will discuss these issues and describe preliminary results
in using a connectionist approach to predict the driver's steer ing
response given vehicle state information. This approach can
successfully detect lane changes, which I treat as surrogate road
departures.
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Parag Batavia, The Robotics Institute, Carnegie Mellon University
parag@ri.cmu.edu
Last modified: Wed Apr 1 09:44:55 EST 1998