The Boeing Company

Hrl Laboratories, LLC

Gm Global Technolgoy Operations, Inc.

Dmitriy V Korchev

Yuri Owechko

Tsai-Ching Lu

Rajan Bhattacharyya

Swarup S Medasani

Charles E Martin

Aruna Jammalamadaka

Alice A Murphy

Steve C Slaughter

Kyungnam Kim

Hyukseong Kwon

Kenji Yamada

Chong Ding

Christopher R Wezdenko

Zhiqi Zhang

Jose M Molineros

Terrell Nathan Mundhenk

Mark A Curry

Alejandro Nijamkin

Hai-Wen Chen

Derek Samuel Fok

Arturo Flores

José M Molineros

Described is a system for analyzing time series data. A sequence of symbols is generated from a set of time series input data related to a moving vehicle using automatic segmentation. A grammar is extracted from the sequence of symbols, and the grammar is a subset of a probabilistic context-free grammar (PCFG). Using the grammar, time series input data can be analyzed, and a prediction of the vehicle's movement can be made. Vehicle operations for an autonomous vehicle are determined using the prediction.

Method for time series structure discovery

A method includes obtaining sensor data captured by a sensor of an aircraft during a power up event. The sensor data includes multiple parameter values, each corresponding to a sample period. The method further includes determining a set of delta values, each indicating a difference between parameter values for consecutive sample periods of the sensor data. The method further includes determining a set of quantized delta values by assigning the delta values to quantization bins based on magnitudes of the delta values. The method further includes determining a normalized count of delta values for each quantization bin. The method further includes comparing the normalized counts of delta values to anomaly detection thresholds. The method further includes generating, based on the comparisons, output indicating whether the sensor data is indicative of an operational anomaly.

Anomaly prediction and detection for aircraft equipment

A fault detection system including one or more sensors onboard a vehicle to detect a characteristic of the vehicle and generate sensor signals corresponding to the characteristic, a processor onboard the vehicle to receive the sensor signals, generate one or more fast Fourier transform vectors based on the sensor signals so that the one or more fast Fourier transform vectors are representative of the characteristic, generate an analysis model from a time history of the fast Fourier transform vectors, and determine, using the analysis model, a degree to which the one or more fast Fourier transform vectors could have been generated by the analysis model, and an indicator to communicate an operational status of the vehicle to an operator or crew member of the vehicle based on the degree to which the one or more fast Fourier transform vectors could have been generated by the analysis model.

Fault detection system and method for vehicle system prognosis

The present application generally relates to a method and apparatus for generating an action policy for controlling an autonomous vehicle. In particular, the method is operative to receive an input indicative of a training event, segmenting the driving episode into a plurality of time steps, generate a parse tree in response to each time step, and generate a most probable parse tree from a combination of the generated parse trees.

Method and apparatus for automatical rule learning for autonomous driving

A method for automatic target recognition in synthetic aperture radar (SAR) data, comprising: capturing a real SAR image of a potential target at a real aspect angle and a real grazing angle; generating a synthetic SAR image of the potential target by inputting, from a potential target database, at least one three-dimensional potential target model at the real aspect angle and the real grazing angle into a SAR regression renderer; and, classifying the potential target with a target label by comparing at least a portion of the synthetic SAR image with a corresponding portion of the real SAR image using a processor.

Methods and systems for model based automatic target recognition in SAR data

A fault detection system including one or more sensors onboard a vehicle, the one or more sensors being configured to detect a predetermined characteristic of the vehicle and generate a plurality of sensor signals corresponding to the predetermined characteristic, and a processor onboard the vehicle and in communication with the one or more sensors, the processor being configured to generate an analysis model for the predetermined characteristic, the analysis model being trained by the processor with a training data set of fast Fourier transform vectors that are generated from the plurality of sensor signals obtained under normal operating conditions of the predetermined characteristic, and determine a health of a vehicle component corresponding to the predetermined characteristic with the analysis model.

According to an embodiment, a method for generating a 3-D stereo structure comprises registering and rectifying a first image frame and a second image frame by local correction matching, extracting a first scan line from the first image frame, extracting a second scan line from the second image frame corresponding to the first scan line, calculating a pixel distance between the first scan line and the second scan line for each pixel for a plurality of pixel shifts, calculating a smoothed pixel distance for each pixel for the pixel shifts by filtering the pixel distance for each pixel over the pixel shifts, and determining a scaled height for each pixel of the first scan line, the scaled height comprising a pixel shift from among the pixel shifts corresponding to a minimal distance of the smoothed pixel distance for the pixel.

Stereo-motion method of three-dimensional (3-D) structure information extraction from a video for fusion with 3-D point cloud data

A method and apparatus for processing video data streams for an area. Objects are identified in the area from images in the video data streams. The video data streams are generated by cameras. First locations are identified for the objects using the images. The first locations are defined using a coordinate system for the images. Graphical representations are formed for the objects using the images. The graphical representations are displayed for the objects in second locations in a model of the area on a display system with respect to features in the area that are represented in the model. The second locations are defined using a geographic coordinate system for the model. A first location in the first locations for an object in the objects corresponds to a second location in the second locations for a corresponding graphical representation in the graphical representations.

Multi-sensor surveillance system with a common operating picture

Systems and methods of detecting dead pixels of image frames are described including receiving a sequence of image frames, aligning, from the sequence of image frames, pairs of image frames, and for a given pair of image frames, determining differences in intensity of corresponding pixels between the aligned pair of image frames. The method also includes, based on the differences in intensity of corresponding pixels between the aligned pair of image frames, generating mask images indicative of areas in the pairs of image frames having moving objects. The method further includes determining, within the mask images, common pixel locations indicative of areas in the pairs of image frames having moving objects over a portion of the sequence of image frames, and based on a number of the common pixel locations for a given pixel location being above a threshold, identifying the given pixel location as a dead pixel.

Methods and systems for detecting moving objects in a sequence of image frames produced by sensors with inconsistent gain, offset, and dead pixels

A motion detector comprising a sensor apparatus and a calculator arranged for: generating first and second successive frames comprising each a 3-D cloud of points, wherein each point represents a position in space of a surface of at least an object in the field of vision of said sensor apparatus; transforming each of the first and second frames by: mapping each cloud of 3-D points into a 2-D grid of cells associated each to a sequence of predetermined volumes; and associating to each cell of the 2-D grid of cells a sequence of the lists of the points contained in corresponding volumes of the sequence of predetermined volumes; comparing the sequences of lists associated to the same cells of the 2-D grids obtained for the first and second frames; and indicating that motion occurred based on the sequence of lists being different in the first and second frames.

Method for finding important changes in 3D point clouds

A method for detecting an opening in a structure represented by a three-dimensional point cloud may include the steps of: (1) creating a three-dimensional point cloud map of a scene, the three-dimensional point cloud map including a plurality of points representing a ground plane and the structure upon the ground plane, (2) identifying an absence of points within the plurality of points representing the structure, and (3) determining whether the absence of points represents the opening in the structure.

System and method for detecting a structural opening in a three dimensional point cloud

A method and target detector for detecting targets. A number of bright pixels are identified in an image. Each of the number of bright pixels belongs to a line detected in the image in which the line represents a candidate for a target. A number of feature vectors are identified for the number of bright pixels in the image. Each of the number of feature vectors is classified as one of a target vector representing the target and a clutter vector representing clutter.

Distinguishing between maritime targets and clutter in range-doppler maps

A method and apparatus for identifying a target object. A group of objects is identified in an image. The group of objects provides a context for identifying the target object in the image. The target object is searched for in the image using the context provided by the group of objects.

Growing community of inventors

Irvine, CA, United States of America

Dmitriy V Korchev