As the evidence of the contribution of intoxicated drivers to vehicular fatalities continues to mount, interest has grown in the development of novel countermeasures. One approach now being considered involves the use of a device installed in the automobile which automatically determines if the driver is intoxicated and prevents the driver from operating the vehicle when intoxication is determined. In this paper such devices are discussed with particular consideration given to the method of determining intoxication, the point in time when the determination is made and their applications. The paper also discusses the Transportation Systems Center's research program directed to dealing with this problem.
Abstract : Imagine a robot that is able to develop skills on its own, without being programmed directly. This robot would be invaluable in any business, factory, or laboratory. Unfortunately, this problem, known as inductive learning, is very difficult, and has several varieties. One such is imitation learning. The overall process of imitation learning begins with one robot observing another robot performing a task. The watcher then breaks down, or segments, the demonstrating robot's actions into basic actions called planning units. Next the observing robot uses the planning units to create a plan that accomplishes the required task. The execution of a successful plan demonstrates that the robot has correctly implemented an inductive learning process. The scope of this research does not allow the problem of imitation learning to be discussed in its entirety; however, it does investigate an important subset of the larger problem. This paper focuses on the segmentation of the data, specifically how to break it up into the steps that provide the building blocks of the robots ultimate plan.
