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.