Motorola Corporation

Other

Motorola Solutions, Inc.

Robert H Leivian

William M Peterson

Robert Michael Gardner

Sidney C Garrison

Clifford A Wood

Donald J Remboski

David John Wheatley

Kenneth W Douros

Joshua B Hurwitz

Jens Nagel

Judith Lee Gardner

Howard C Anderson

Christopher N Hume

A method of assessing vehicle operator performance includes the steps of receiving vehicle operating data (); monitoring an interior portion of the vehicle and receiving operator activity data from the interior portion vehicle (); receiving vehicle environment data from the environment external to the vehicle (); monitoring the vehicle operator and receiving operator condition data (); and determining an operator assessment value (). The operator assessment value is based upon the vehicle operating data, the operator activity data, the environment data and the operator condition data and is indicative of vehicle operator performance.

Method and apparatus for vehicle operator performance assessment and improvement

A method () of informing a vehicle operator to improve the operator's performance includes the steps of receiving vehicle operating data (); monitoring an interior portion of the vehicle and receiving operator activity data from the interior portion of the vehicle (); receiving vehicle environment data (); monitoring the vehicle operator and receiving operator condition data relating to a condition of the vehicle operator (); estimating an operator cognitive load (); and prioritizing vehicle information based upon the operator cognitive load for selectively informing the operator of the vehicle information ().

System and method for driver performance improvement

A method ( ) of synthesizing a response to an operating situation of a vehicle includes the step of generating a master condition list ( ), determining the existence of an operating situation ( ), determining an operator cognitive load ( ) and determining a response to the operating situation based in part on the master condition list and the operator cognitive load ( ). The master condition list is a fusion of sensor data gathered within and around the vehicle. The operator cognitive load is a measure of the operator's ability to handle tasks within the vehicle.

Method of response synthesis in a driver assistance system

A method including providing a database with a plurality of items, each item having several attributes, and developing and displaying a cluster map (SOM) attribute plane from the items and attributes, including clusters of items having similar attributes. Selecting an attribute of interest and splitting the plurality of items into clusters of interest based on distribution of the selected attribute. Utilizing rule induction to provide a rule for each cluster of interest, drawing a line around adjacent items in the SOM attribute plane for each rule to form at least one complete fenced area in the display, and comparing fenced areas to clusters to separate statistically meaningful rules from statistically meaningless rules or coincidences.

Method of determining statistically meaningful rules

A neural network (10) organizes the data items into a graphically oriented format by retrieving data items from a database (68) where each data item has a plurality of attributes. The neural network is organized (102) such that data items having similar attributes are assigned to neurons located closer together. The neurons of the neural network are matched (104) with the data items from the database and stored in a cross reference table. The cross reference table is displayed (106) on a computer screen (108) in a graphical format so that user visually relates the food items and sees the similarities and differences in their attribute data by the proximity of the data items to one another. The graphic format allows easy visual interpretation of the data items. For large databases, multiple neural networks (110, 112) can be organized hierarchically.

Method of organizing data into a graphically oriented format

A computer simulator is provided for displaying the state of an artificial neural network in a simplified yet meaningful manner on a computer display terminal. The user may enter commands to select one or more areas of interest within the neural network for further information regarding its state of learning and operation. One display mode illustrates the output activity of each neuron as representatively sized and shaded boxes within the border of the neuron, while another display mode shows the connectivity as weighted synapses between a user-selected neuron and the remaining neurons of the network in a similar manner. A third display mode provides a tuning curve wherein the synapses associated with each of the neurons are represented within the borders of the same. Both grid block and line graph type characterization are supported. The methodology allows large neural networks on the order of thousands of neurons to be displayed in a meaningful manner.

Method of displaying the state of an artificial neural network

An artificial neural network is provided using a digital architecture having feedforward and feedback processors interconnected with a digital computation ring or data bus to handle complex neural feedback arrangements. The feedforward processor receives a sequence of digital input signals and multiplies each by a weight in a predetermined manner and stores the results in an accumulator. The accumulated values may be shifted around the computation ring and read from a tap point thereof, or reprocessed through the feedback processor with predetermined scaling factors and combined with the feedforward outcomes for providing various types neural network feedback computations. Alternately, the feedforward outcomes may be placed sequentially on a data bus for feedback processing through the network. The digital architecture includes a predetermined number of data input terminals for the digital input signal irrespective of the number of synapses per neuron and the number of neurons per neural network, and allows the synapses to share a common multiplier and thereby reduce the physical area of the neural network. A learning circuit may be utilized in the feedforward processor for real-time updating the weights thereof to reflect changes in the environment.

Digital processing element in an artificial neural network

An artificial neuron is provided using a simple distance calculation between the input signal vector and the synapse weight signals for providing an output signal. A difference signal is developed by subtracting a weight signal from an input signal. The difference signal is processed through a weighting function having a predetermined polarity and accumulated for providing the output signal of the neuron. A digital embodiment is supported with a memory circuit for storing the digital weights and a memory lookup table or possibly a multiplexer circuit for weighting of the difference signal. An analog embodiment uses a plurality of comparators responsive to the input signal vector and the weight signals for providing the output signal of the neuron as the absolute value of the difference of the input signal vectors and the weight signals.

Simple distance neuron

An artificial neural network is provided using a modular, self-organizing approach wherein a separate neural field is contained within each module for recognition and synthesis of particular characteristics of respective input and output signals thereby allowing several of these modules to be interconnected to perform a variety of operations. The first output and second input of one module is respectively coupled to the first input and second output of a second module allowing each module to perform a bi-directional transformation of the information content of the first and second input signals for creating first and second output signals having different levels of information content with respect thereto. In the upward direction, the first low-level input signal of each module is systematically delayed to create a temporal spatial vector from which a lower frequency, high-level first output signal is provided symbolic of the incoming information content. Since the first output signal contains the same relevant information as the first input signal while operating at a lower frequency, the information content of the latter is said to be compressed into a first high-level output signal. In the downward direction, a second output signal having a low-level of information content is synthesized from a second input signal having a high-level of information content. The second input signal is the best prediction of the first output signal available from the knowledge base of the module, while similarly the second output signal is the prediction of the first input signal.

SPANN: Sequence processing artificial neural network

A neural network is provided for performing bi-directional signal transformations through a matrix of synapses by alternately sending and receiving signal vectors therethrough via switchable driver circuits. In the forward direction, the input signal is transformed according to the weighting elements of the synapses for providing an output signal. The drive direction of the switchable driver circuits may be reversed allowing the output signal to flow back through the same synapses thereby performing a reverse transformation, which may actually be an improved estimate of the original input signal. Sample and hold circuits are provided for latching the output signals of the switchable driver circuits back to the inputs thereof for repeated forward and reverse signal transformations until an acceptable transformation of the original input signal is realized, thereby achieving an improved estimate of the input signal and corresponding output transformation. More generally, a first input signal may be transformed in one direction through the synapses, while a second input signal, possibly independent and unrelated to the first input signal, may be reverse transformed in the opposite direction using the same synapses as the first direction.

Reverse flow neuron

A random access memory (RAM) circuit is provided wherein an input signal matrix forming an identifiable original pattern is learned and stored such that a distorted facsimile thereof may be applied to generate an output signal matrix forming a replication of the original pattern having improved recognizable features over the distorted facsimile. The input signal matrix is logically divided into a plurality of predetermined subsets comprising a unique element of the input signal matrix and the elements in the neighborhood thereof. Each predetermined subset is quantized into a first digital address and applied at the address inputs of a memory circuit for retrieving data stored in the addressed memory location, while one signal of the predetermined subset is digitized and weighted and combined with the data retrieved from the addressed memory location for storage in the same addressed memory location. Next, a plurality of second digital addresses is generated including predetermined combinations of the first digital address perturbed at least one bit and sequentially applied at the address inputs of the memory circuit whereby the steps of digitizing and weighting one signal of the predetermined subset of the input signal matrix, combining the digitized and weighted signal with the data retrieved from the addressed memory location, and storing the combination back into the addressed memory location are repeated for the second digital addresses.

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Chandler, AZ, United States of America

Robert H Leivian