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公开(公告)号：US20220126863A1
公开(公告)日：2022-04-28
申请号：US17434716
申请日：2020-03-27
Applicant: Intel Corporation
Inventor： Hassnaa Moustafa , Soila P. Kavulya , Igor Tatourian , Rita H. Wouhaybi , Ignacio J. Alvarez , Fatema S. Adenwala , Cagri C. Tanriover , Maria S. Elli , David J. Zage , Jithin Sankar Sankaran Kutty , Christopher E. Lopez-Araiza , Magdiel F. Galán-Oliveras , Li Chen
Abstract: An apparatus comprising at least one interface to receive a signal identifying a second vehicle in proximity of a first vehicle; and processing circuitry to obtain a behavioral model associated with the second vehicle, wherein the behavioral model defines driving behavior of the second vehicle; use the behavioral model to predict actions of the second vehicle; and determine a path plan for the first vehicle based on the predicted actions of the second vehicle.
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22.发明申请公开(公告)号：US20210309261A1
公开(公告)日：2021-10-07
申请号：US17352560
申请日：2021-06-21
Applicant: Intel Corporation
Inventor： Rafael Rosales , Ignacio J. Alvarez , Florian Geissler , Neslihan Kose Cihangir , Michael Paulitsch
Abstract: Techniques are disclosed to detect, inform, and automatically correct typical awareness-related human driver mistakes. This may include those that are caused by a misunderstanding of the current situation, a lack of focus or attention, and/or overconfidence in any currently-engaged assistance features. The disclosure is directed to the prediction of vehicle maneuvers using driver and external environment modeling. The consequence of executing a predicted maneuver is categorized based upon its risk or danger posed to the driving environment, and the vehicle may execute various actions based upon the categorization of a predicted riving maneuver to mitigate or eliminate that risk.
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23.发明申请公开(公告)号：US20190228262A1
公开(公告)日：2019-07-25
申请号：US16370988
申请日：2019-03-30
Applicant: Intel Corporation
Inventor： Domingo C. Gonzalez , Ignacio J. Alvarez , Mehrnaz Khodam Hazrati , Christopher Lopez-Araiza
IPC: G06K9/62 , G06K9/00 , A63F13/23 , A63F13/46 , A63F13/533
Abstract: Techniques are disclosed herein for collecting annotation data via a gamified user interface in a vehicle control system. According to an embodiment disclosed herein, the vehicle control system detects a trigger to initiate an annotation prompt associated with an object classified from an image. The vehicle control system presents, via a user interface, the annotation prompt. The vehicle control system receives, via the user interface, user input indicative of a response to the annotation prompt by a user and updates a confidence score associated with the classified object as a function of one or more metrics associated with the user.
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公开(公告)号：US20190226868A1
公开(公告)日：2019-07-25
申请号：US16370986
申请日：2019-03-30
Applicant: Intel Corporation
Inventor： Mehrnaz Khodam Hazrati , Ignacio J. Alvarez , Domingo C. Gonzalez , Christopher Lopez-Araiza
Abstract: Technologies for intelligent traffic optimization include a directional flow server that receives dynamic traffic data from traffic infrastructure devices in a monitored region. The traffic data may include traffic volume data and traffic control status data. The server updates a dynamic layer of a high-definition map based on the dynamic traffic data. The high-definition map also includes a static layer and a directional flow layer. The server optimizes the directional flow in response to updating the dynamic layer. The directional flow layer is indicative of traffic direction associated with roads in the monitored region. The server may optimize each of several sub-regions and then optimize connection roads between the regions. The server may distribute the optimized directional flow layer to consumers such as traffic control devices, autonomous vehicles, and other subscribing devices. Other embodiments are described and claimed.
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公开(公告)号：US20190197029A1
公开(公告)日：2019-06-27
申请号：US16326878
申请日：2016-12-22
Applicant: Intel Corporation
Inventor： David I. Gonzalez Aguirre , Ignacio J. Alvarez , Javier Felip Leon
CPC classification number: G06F16/2264 , G06F16/2246 , G06F16/2365 , G06F16/2477
Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to improve spatial-temporal data management. An example apparatus includes a hypervoxel data structure generator to generate a root hexatree data structure having sixteen hypernodes, an octree manager to improve a spatiotemporal data access efficiency by generating a first degree of symmetry in the root hexatree, the octree manager to assign a first portion of the hypernodes to a positive temporal subspace and to assign a second portion of the hypernodes to a negative temporal subspace, and a quadtree manager to improve the spatiotemporal data access efficiency by generating a second degree of symmetry in the root hexatree, the quadtree manager to assign respective hypernodes of the positive temporal subspace and the negative temporal subspace to respective positive and negative spatial subspaces.
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Patent Agency Ranking
公开(公告)号：US10204422B2
公开(公告)日：2019-02-12
申请号：US15412853
申请日：2017-01-23
Applicant: Intel Corporation
Inventor： David I. Gonzalez Aguirre , Javier Felip Leon , Ignacio J. Alvarez
Abstract: An example system for generating a three dimensional (3D) model includes a receiver to receive a single two dimensional (2D) image of an object to be modeled. The system includes a segment extractor to extract a binary segment, a textured segment, and a segment characterization based on the single 2D image. The system further includes a skeleton cue extractor to generate a medial-axis transform (MAT) approximation based on the binary segment and the segment characterization and extract a skeleton cue and a regression cue from the MAT approximation. The system also includes a contour generator to generate a contour based on the binary segment and the regression cue. The system can also further include a 3D model generator to generate a 3D model based on the contour and the skeleton cue.
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公开(公告)号：US20180211438A1
公开(公告)日：2018-07-26
申请号：US15412853
申请日：2017-01-23
Applicant: Intel Corporation
Inventor： David I. Gonzalez Aguirre , Javier Felip Leon , Ignacio J. Alvarez
Abstract: An example system for generating a three dimensional (3D) model includes a receiver to receive a single two dimensional (2D) image of an object to be modeled. The system includes a segment extractor to extract a binary segment, a textured segment, and a segment characterization based on the single 2D image. The system further includes a skeleton cue extractor to generate a medial-axis transform (MAT) approximation based on the binary segment and the segment characterization and extract a skeleton cue and a regression cue from the MAT approximation. The system also includes a contour generator to generate a contour based on the binary segment and the regression cue. The system can also further include a 3D model generator to generate a 3D model based on the contour and the skeleton cue.
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28.发明公开公开(公告)号：US20240112506A1
公开(公告)日：2024-04-04
申请号：US17936438
申请日：2022-09-29
Applicant: Intel Corporation
Inventor： Ned M. Smith , S M Iftekharul Alam , Ignacio J. Alvarez , Kshitij Doshi , Francesc Guim Bernat , Satish Jha , Arvind Merwaday , Vesh Raj Sharma Banjade , Kathiravetpillai Sivanesan
CPC classification number: G07C5/008 , H04W74/002
Abstract: A DTaaS architecture is described to support communication-side optimization and to reduce communication overhead while meeting necessary reliability and latency requirements. The disclosure describe techniques for utilizing DT resources for V2X environments using both virtual and physical “twin” resources to achieve improved resiliency. Moreover, to optimize redundancy costs, the ratio of virtual DT nodes to physical DT nodes may be asymmetrical. An asymmetric approach to DT redundancies involving both virtual and physical resources enables greater flexibility in managing deployment costs.
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29.发明授权公开(公告)号：US11702105B2
公开(公告)日：2023-07-18
申请号：US16914298
申请日：2020-06-27
Applicant: Intel Corporation
Inventor： Ignacio J. Alvarez , Vy Vo , Javier Felip Leon , Javier Perez-Ramirez , Javier Sebastian Turek , Mariano Tepper , David Israel Gonzalez Aguirre
CPC classification number: B60W60/0015 , B60W30/09 , B60W30/0956 , B60W40/06 , B60W60/0011 , G01C21/3407 , G06N3/08 , G06N5/02 , B60W2552/00 , B60W2554/4041 , B60W2554/4042 , B60W2555/00
Abstract: Systems, apparatuses and methods may provide for technology that generates, via a first neural network such as a grid network, a first vector representing a prediction of future behavior of an autonomous vehicle based on a current vehicle position and a vehicle velocity. The technology may also generate, via a second neural network such as an obstacle network, a second vector representing a prediction of future behavior of an external obstacle based on a current obstacle position and an obstacle velocity, and determine, via a third neural network such as a place network, a future trajectory for the vehicle based on the first vector and the second vector, the future trajectory representing a sequence of planned future behaviors for the vehicle. The technology may also issue actuation commands to navigate the autonomous vehicle based on the future trajectory for the vehicle.
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公开(公告)号：US11699085B2
公开(公告)日：2023-07-11
申请号：US16894535
申请日：2020-06-05
Applicant: Intel Corporation
Inventor： Glen J. Anderson , Rajesh Poornachandran , Ignacio J. Alvarez , Giuseppe Raffa , Jill Boyce , Ankur Agrawal , Kshitij Arun Doshi
Abstract: Logic may determine a specific performance of a neural network based on an event and may present the specific performance to provide a user with an explanation of the inference by a machine learning model such as a neural network. Logic may determine a first activation profile associated with the event, the first activation profile based on activation of nodes in one or more layers of the neural network during inference to generate an output. Logic may correlate the first activation profile against a second activation profile associated with a first training sample of training data. Logic may determine that the first training sample is associated with the event based on the correlation. Logic may output an indicator to identify the first training sample as being associated with the event.
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