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公开(公告)号：US20230351544A1
公开(公告)日：2023-11-02
申请号：US18350354
申请日：2023-07-11
Applicant: Ford Global Technologies, LLC.
Inventor： Vidya Nariyambut Murali , Madeline Jane Schrier
CPC classification number: G06T1/20 , G06V10/454 , G06V20/582 , G06V10/87 , G06V30/19113 , G06F18/285 , G06V10/764 , G06V10/82 , G06T2207/30252
Abstract: Disclosures herein teach applying a set of sections spanning a down-sampled version of an image of a road-scene to a low-fidelity classifier to determine a set of candidate sections for depicting one or more objects in a set of classes. The set of candidate sections of the down-sampled version may be mapped to a set of potential sectors in a high-fidelity version of the image. A high-fidelity classifier may be used to vet the set of potential sectors, determining the presence of one or more objects from the set of classes. The low-fidelity classifier may include a first Convolution Neural Network (CNN) trained on a first training set of down-sampled versions of cropped images of objects in the set of classes. Similarly, the high-fidelity classifier may include a second CNN trained on a second training set of high-fidelity versions of cropped images of objects in the set of classes.
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公开(公告)号：US11220259B2
公开(公告)日：2022-01-11
申请号：US16400763
申请日：2019-05-01
Applicant: Ford Global Technologies, LLC.
Inventor： Sneha Kadetotad , Jinesh J Jain , Vidya Nariyambut Murali , Dongran Liu , Marcos Paul Gerardo Castro , Adil Nizam Siddiqui
Abstract: A controller receives outputs from a plurality of sensors such as a camera, LIDAR sensor, RADAR sensor, and ultrasound sensor, which may be rearward facing. A probability is updated each time a feature in a sensor output indicates presence of an object. The probability may be updated as a function of a variance of the sensor providing the output and a distance to the feature. Where the variance of a sensor is directional, directional probabilities may be updated according to these variances and the distance to the feature. If the probability meets a threshold condition, actions may be taken such as a perceptible alert or automatic braking. The probability may be decayed in the absence of detection of objects. Increasing or decreasing trends in the probability may be amplified by further increasing or decreasing the probability.
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公开(公告)号：US11200447B2
公开(公告)日：2021-12-14
申请号：US16444301
申请日：2019-06-18
Applicant: Ford Global Technologies, LLC.
Inventor： Vidya Nariyambut Murali , Madeline Jane Schrier
Abstract: Disclosures herein teach applying a set of sections spanning a down-sampled version of an image of a road-scene to a low-fidelity classifier to determine a set of candidate sections for depicting one or more objects in a set of classes. The set of candidate sections of the down-sampled version may be mapped to a set of potential sectors in a high-fidelity version of the image. A high-fidelity classifier may be used to vet the set of potential sectors, determining the presence of one or more objects from the set of classes. The low-fidelity classifier may include a first Convolution Neural Network (CNN) trained on a first training set of down-sampled versions of cropped images of objects in the set of classes. Similarly, the high-fidelity classifier may include a second CNN trained on a second training set of high-fidelity versions of cropped images of objects in the set of classes.
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公开(公告)号：US20210264284A1
公开(公告)日：2021-08-26
申请号：US16800950
申请日：2020-02-25
Applicant: Ford Global Technologies, LLC
Inventor： Shubh Gupta , Nikita Jaipuria , Praveen Narayanan , Vidya Nariyambut Murali
Abstract: The present disclosure discloses a system and a method. In an example implantation, the system and the method can generate, at a discriminator, a plurality of image patches from an image, determine a plurality of routing coefficients within a capsule network based on the plurality of image patches, generate a prediction indicating whether the image is synthetic or sourced from a real distribution based on the plurality of routing coefficients, and update one or more weights of a generator based on the prediction, wherein the generator is connected to the discriminator.
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公开(公告)号：US10977783B1
公开(公告)日：2021-04-13
申请号：US16653182
申请日：2019-10-15
Applicant: Ford Global Technologies, LLC
Inventor： Nikita Jaipuria , Gautham Sholingar , Vidya Nariyambut Murali
Abstract: The present disclosure discloses a system and a method. In an example implementation, the system and the method can receive a synthetic image at a first deep neural network, and determine, via the first deep neural network, a prediction indicative of whether the synthetic image is machine-generated or is sourced from the real data distribution. The prediction can comprise a quantitative measure of photorealism of synthetic image.
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Patent Agency Ranking
公开(公告)号：US20190256089A1
公开(公告)日：2019-08-22
申请号：US16400763
申请日：2019-05-01
Applicant: Ford Global Technologies, LLC.
Inventor： Sneha Kadetotad , Jinesh J. Jain , Vidya Nariyambut Murali , Dongran Liu , Marcos Paul Gerardo Castro , Adil Nizam Siddiqui
Abstract: A controller receives outputs from a plurality of sensors such as a camera, LIDAR sensor, RADAR sensor, and ultrasound sensor, which may be rearward facing. A probability is updated each time a feature in a sensor output indicates presence of an object. The probability may be updated as a function of a variance of the sensor providing the output and a distance to the feature. Where the variance of a sensor is directional, directional probabilities may be updated according to these variances and the distance to the feature. If the probability meets a threshold condition, actions may be taken such as a perceptible alert or automatic braking. The probability may be decayed in the absence of detection of objects. Increasing or decreasing trends in the probability may be amplified by further increasing or decreasing the probability.
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公开(公告)号：US10229363B2
公开(公告)日：2019-03-12
申请号：US14887031
申请日：2015-10-19
Applicant: Ford Global Technologies, LLC
Inventor： Vidya Nariyambut Murali , Sneha Kadetotad , Daniel Levine
Abstract: Systems, methods, and devices for sensor fusion are disclosed herein. A system for sensor fusion includes one or more sensors, a model component, and an inference component. The model component is configured to calculate values in a joint-probabilistic graphical model based on the sensor data. The graphical model includes nodes corresponding to random variables and edges indicating correlations between the nodes. The inference component is configured to detect and track obstacles near a vehicle based on the sensor data and the model using a weighted-integrals-and-sums-by-hashing (WISH) algorithm.
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公开(公告)号：US10228693B2
公开(公告)日：2019-03-12
申请号：US15406031
申请日：2017-01-13
Applicant: Ford Global Technologies, LLC
Inventor： Ashley Elizabeth Micks , Sneha Kadetotad , Jinesh J. Jain , Dongran Liu , Marcos Paul Gerardo Castro , Vidya Nariyambut Murali
Abstract: A scenario is defined that including models of vehicles and a typical driving environment. A model of a subject vehicle is added to the scenario and sensor locations are defined on the subject vehicle. Perception of the scenario by sensors at the sensor locations is simulated to obtain simulated sensor outputs. The simulated sensor outputs are annotated to indicate the location of obstacles in the scenario. The annotated sensor outputs may then be used to validate a statistical model or to train a machine learning model. The simulates sensor outputs may be modeled with sufficient detail to include sensor noise or may include artificially added noise to simulate real world conditions.
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公开(公告)号：US20190065944A1
公开(公告)日：2019-02-28
申请号：US15687247
申请日：2017-08-25
Applicant: Ford Global Technologies, LLC
Inventor： Guy Hotson , Vidya Nariyambut Murali , Gintaras Vincent Puskorius
Abstract: A system includes a processor for performing one or more autonomous driving or assisted driving tasks based on a neural network. The neural network includes a base portion for performing feature extraction simultaneously for a plurality of tasks on a single set of input data. The neural network includes a plurality of subtask portions for performing the plurality of tasks based on feature extraction output from the base portion. Each of the plurality of subtask portions comprise nodes or layers of a neutral network trained on different sets of training data, and the base portion comprises nodes or layers of a neural network trained using each of the different sets of training data constrained by elastic weight consolidation to limit the base portion from forgetting a previously learned task.
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公开(公告)号：US10198655B2
公开(公告)日：2019-02-05
申请号：US15414383
申请日：2017-01-24
Applicant: Ford Global Technologies, LLC
Inventor： Guy Hotson , Parsa Mahmoudieh , Vidya Nariyambut Murali
Abstract: According to one embodiment, a system includes a sensor component and a detection component. The sensor component is configured to obtain a first stream of sensor data and a second stream of sensor data, wherein each of the first stream and second stream comprise a plurality of sensor frames. The detection component is configured to generate a concatenated feature map based on a sensor frame of a first type and a sensor frame of a second type. The detection component is configured to detect one or more objects based on the concatenated feature map. One or more of generating and detecting comprises generating or detecting using a neural network with a recurrent connection that feeds information about features or objects from previous frames.
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