Daimler Chrysler Ag

Daimler Ag

Abb Daimler-benz Transportation (technology) Gmbh

Dietmar Neumerkel

Klaus-Peter Kuhn

Lars Galley

Arnim Lars Galley

Elisabeth Bukman

Elisabeth Hentschel-Bukman

Thomas Gr&uuml;nder

A method and a computer program for identifying when the driver of a vehicle, in particular a motor vehicle, is not paying attention. In order to make it possible to make a more reliable statement on the presence of any inattentiveness by the driver, the method observes a steering action following a steering quiescent phase, in addition to detecting the steering quiescent phase. The extent of the steering quiescent phase and of the steering action found are logically linked to one another, and the result of this logical operation is then used as a measure for the severity of the inattentiveness of the driver.

Method and computer program for identification of inattentiveness by the driver of a vehicle

In a method and a computer program for identification when the driver of a vehicle is inattentive, a steering action following a steering quiescent phase is observed, in addition to the steering quiescent phase itself. The extent of the steering quiescent phase and of the steering action found are logically linked to one another, and the result of this logical operation is then used as a measure for the severity of the inattentiveness of the driver.

In a method for the probabilistic estimation of measurements, based on a measurement signal in which an interference signal is superimposed on the value to be measured, the measurement signal is sampled at specified chronological intervals. A defined measurement range associated with the value to be measured is divided into discrete values and a model is formed of a process on which the measurement signal is based with discrete states that correspond to the discrete values of the measurement range. In the model, a probability value of the occurrence of each state is assigned for each sampling time, and the value to be measured is determined on the basis of the probability value of at least one state in this model. In addition, for each state of the model at a sampling time, a probability for the state to remain in its current state is determined, as well as a probability for the state to change to another state by the next sampling time. On the basis of the value of the measurement signal sampled in a current sampling time, the probability value for the occurrence of the states in the preceding sampling time and the probability for each state to remain unchanged and to change to another state between the two sampling times, the probability values for the occurrence of the states of the model for the current sampling time are recalculated.

Method for the probabilistic estimation of noisy measurements

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Berlin, Germany

Dietmar Neumerkel