Intel Corporation

Mobileye Vision Technologies Ltd.

Rita Chattopadhyay

Monica Lucia Martinez-Canales

Ravindra V Narkhede

Soila Pertet Kavulya

Subramanian Anandaraj

Magdiel F Galan-Oliveras

Hassnaa Moustafa

Vinod Sharma

Ruchika Singh

Kalpana A Algotar

Pradeep Sakhamoori

Anne M Russell

Tina Alam

Nikhil Jog

Andal Jayalakshmi Ganapathy Ramalingam

Francesc Guim Bernat

Cornelius Buerkle

Malini Krishnan Bhandaru

Fabian Oboril

Peggy J Irelan

Sangeeta Ghangam Manepalli

Frederik Pasch

Siew Wen Chin

Kay-ulrich Scholl

Jeffrey B Davis

Paul Hough

Ali Ashrafi

Rony Ferzli

Tomasz Janusz Wolak

Amith Harsha

Tara K Thimmanaik

Hoang Van

David J Austin

Tomasz J Wolak

Florian Mirus

Thierry Beaumont

Mandar Chincholkar

Trevor Ryan

David M Bayba

Xiaozhong Ji

Stephanie Cope

Mruthunjaya Chetty

Petek Yontay

John Pilkin

Paul F Hough

Hoang T Van

Various systems and methods for detecting risk conditions in a physical workspace. An apparatus can include an interface to receive smart sensor signals from at least one autonomous mobile entity (AME) in the physical workspace. The apparatus can also include processing circuitry coupled to the interface to detect a risk condition associated with the at least one AME, based on the smart sensor signals, relative to a user device associated with a human present in the physical workspace. The processing circuitry can also detect a direction of the risk condition relative to the user device and cause a notification to the first user device. The notification can indicate the direction of the risk condition relative to the user device. Other systems, methods and apparatuses are described.

Autonomous mobile entity (AME) risk monitoring and maneuvering systems

A robot including a resource sharing circuit, configured to operate according to a resource utilization limit, and comprising a processor, configured to receive resource data representing a resource utilization of the robot; if the resource utilization of the robot is within a predetermined range, operate according to a first operational mode, wherein an upper limit of the predetermined range is defined by a tolerance relative to the resource utilization limit; if the resource utilization of the robot is outside of the predetermined range, operate according to a second operational mode; wherein the first operational mode includes controlling a communication circuit to send a first wireless signal representing an availability to accept a task; and wherein the second operational mode includes controlling the communication circuit to send a second wireless signal representing an availability to allocate a task to an external device for remote processing.

Autonomous mobile robot scaling

Various aspects of techniques, systems, and use cases include provide instructions for operating an autonomous mobile robot (AMR). A technique may include capturing audio or video data using a sensor of the AMR, performing a classification of the audio or video data using a trained classifier, and identifying a coordinate of an environmental map corresponding to a location of the audio or video data. The technique may include updating the environmental map to include the classification as metadata corresponding to the coordinate. The technique may include communicating the updated environmental map to an edge device.

Scene intelligence for collaborative semantic mapping with mobile robots

In one embodiment, a device comprises interface circuitry and processing circuitry. The processing circuitry receives, via the interface circuitry, a video stream captured by a camera during performance of an industrial process, wherein the video stream comprises a sequence of frames; detects, based on analyzing the sequence of frames, a degree of particle scatter that occurs during performance of the industrial process; and determines, based on the degree of particle scatter, that an anomaly occurs during performance of the industrial process.

Real-time anomaly detection for industrial processes

Technologies for performing sensor fusion include a compute device. The compute device includes circuitry configured to obtain detection data indicative of objects detected by each of multiple sensors of a host system. The detection data includes camera detection data indicative of a two or three dimensional image of detected objects and lidar detection data indicative of depths of detected objects. The circuitry is also configured to merge the detection data from the multiple sensors to define final bounding shapes for the objects.

Technologies for fusing data from multiple sensors to improve object detection, identification, and localization

A device for a vehicle may include a first wireline interface configured to receive a first data stream from a first sensor having a first sensor type for perceiving a surrounding of the vehicle, the first data stream including raw sensor data detected by the first sensor; a second wireline interface configured to receive a second data stream from a second sensor having a second sensor type for perceiving the surrounding of the vehicle, the second data stream including raw sensor data detected by the second sensor; one or more processors configured to generate a coded packet including the received first data stream and the received second data stream by employing vector packet coding on the first data stream and the second data stream; and an output wireline interface configured to transmit the generated coded packet to one or more target units of the vehicle.

Device for a vehicle

Apparatus and method to facilitate automatic detection of a device state are disclosed herein. Selectively constraining a sensor based data set associated with one or more states of a device, wherein selectively constraining the sensor based data set includes analyzing a distribution of the sensor based data set to determine whether to constrain the sensor based data set, the sensor based data set including a first class and a second class of data values. Determining a threshold associated with the sensor based data set by selecting the threshold based on a variance between the first and second classes of the sensor based data set, wherein selecting the threshold includes using a constrained sensor based data set when the sensor based data set is determined to be constrained, and wherein the threshold indicates the data values associated with the first and second classes.

Sensor based data set method and apparatus

Spatial data may be divided along an axis of the second dimension into a first data segment and a second data segment, such that the first data segment is limited to data points of the spatial data with second dimension coordinates within a first range and the second data segment is limited to data points of the spatial data with second dimension coordinates within a second range. A first processing element may execute an object detection process on the first data segment to generate a first list of objects within the first data segment. A second processing element may execute the object detection process on the second data segment to generate a second list of objects within the second data segment. A first set of objects detected in the first data segment may be combined with a second set of objects detected in the second data segment.

Acceleration of data processing for object detection

Various systems and methods for implementing LiDAR-based object detection and classification are described herein. An object detection system includes a feature extraction and object identification (FEOI) circuit to: receive segmented data of an environment around the object detection system, the segmented data obtained using a light imaging detection and ranging (LiDAR) system, oriented with respect to a direction of travel; compute spatial and structural parameters of a segment of the segmented data; and use the spatial and structural parameters with a machine learning model to obtain a classification of the segment.

LiDAR-based object detection and classification

Methods, systems and apparatuses may provide for technology that conducts an automated vision analysis of image data associated with an interior of a vehicle cabin, determines a state of a child restraint system (CRS) based on the automated vision analysis, and generates an alert if the state of the CRS does not satisfy one or more safety constraints. In one example, the technology identifies the safety constraint(s) based on a geographic location of the vehicle cabin.

Alert system technology to conduct in-cabin child restraint system monitoring and facilitate compliance with location-dependent constraints

Technologies for autonomous vehicle driving quality of service (QoS) determination and communication include an advanced vehicle with a vehicle computing device. The computing device determines multiple route segments of one or more routes to a destination. The computing device determines, for each route segment, one or more autonomous driving factors that are each indicative of an autonomy level achievable by the advanced vehicle for the associated route segment. Factors may include map availability, weather conditions, road conditions, or other factors. The computing device determines, for each route segment, an autonomous QoS score based on the autonomous driving factors. The computing device may rank multiple routes to the destination based on the autonomous QoS scores associated with the route segments of those routes. The computing device may display a proposed route to a user of the advanced vehicle and receive a selection from the user. Other embodiments are described and claimed.

Technologies for autonomous driving quality of service determination and communication

Technologies for efficient identity recognition based on skin features include a compute device. The compute device includes an image capture device and an image acquisition module to obtain, with the image capture device, an image that depicts the skin of the person. The compute device also includes a skin feature determination module to identify pixels in the obtained image that are associated with the skin of the person, and determine one or more features of the skin based on the identified pixels. Additionally, the compute device includes an identity determination module to generate a feature vector that includes the determined features of the skin, and analyze the feature vector with reference data to determine an identity of the person. Other embodiments are described and claimed.

Technologies for efficient identity recognition based on skin features

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Chandler, AZ, United States of America

Rita Chattopadhyay