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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
摘要： 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：US20170206426A1
公开(公告)日：2017-07-20
申请号：US14997120
申请日：2016-01-15
CPC分类号： G06K9/00805 , B60W50/00 , G05D1/0088 , G06K9/6273 , G06K9/6281 , G06N3/0454 , G06N3/08 , G06T2207/10004 , G06T2207/20084 , G06T2207/30196 , G06T2207/30252
摘要： Systems, methods, and devices for pedestrian detection are disclosed herein. A method includes receiving an image of a region near a vehicle. The method further includes processing the image using a first neural network to determine one or more locations where pedestrians are likely located within the image. The method also includes processing the one or more locations of the image using a second neural network to determine that a pedestrian is present and notifying a driving assistance system or automated driving system that the pedestrian is present.
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公开(公告)号：US20190311221A1
公开(公告)日：2019-10-10
申请号：US16444301
申请日：2019-06-18
摘要： 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：US10410522B2
公开(公告)日：2019-09-10
申请号：US15761705
申请日：2015-10-28
摘要： Example systems and methods for communicating animal proximity to a vehicle are described. In one implementation, a device implanted in an animal is activated when the device is within a predetermined distance of a vehicle. The vehicle receives a signal from the device and determines an approximate distance between the device and the vehicle. A symbol is flashed to a driver of the vehicle at a frequency that corresponds to the approximate distance between the device and the vehicle.
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公开(公告)号：US20170206434A1
公开(公告)日：2017-07-20
申请号：US14995482
申请日：2016-01-14
CPC分类号： G06K9/628 , G06K9/00818 , G06K9/00993 , G06K9/4628 , G06K9/4642 , G06K9/6232 , G06K9/6256 , G06K9/627 , G06K9/6273 , G06T1/20 , G06T7/70 , G06T2207/20084 , G06T2207/30252
摘要： 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：US20210334610A1
公开(公告)日：2021-10-28
申请号：US17371866
申请日：2021-07-09
摘要： The disclosure extends to methods, systems, and apparatuses for automated fixation generation and more particularly relates to generation of synthetic saliency maps. A method for generating saliency information includes receiving a first image and an indication of one or more sub-regions within the first image corresponding to one or more objects of interest. The method includes generating and storing a label image by creating an intermediate image having one or more random points. The random points have a first color in regions corresponding to the sub-regions and a remainder of the intermediate image having a second color. Generating and storing the label image further includes applying a Gaussian blur to the intermediate image.
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公开(公告)号：US09937922B2
公开(公告)日：2018-04-10
申请号：US14876269
申请日：2015-10-06
IPC分类号： B60W30/09 , G05D1/00 , G06F19/00 , B60W10/04 , B60W10/18 , B60W10/20 , H04R1/32 , G05D1/02 , G01S13/93
CPC分类号： B60W30/09 , B60W10/04 , B60W10/18 , B60W10/20 , B60W2420/10 , B60W2420/54 , B60W2550/14 , B60W2710/18 , B60W2710/20 , B60W2720/10 , G01S5/16 , G01S5/28 , G01S2013/9357 , G01S2013/936 , G05D1/0088 , G05D1/0255 , G05D1/0274 , G05D2201/0213 , H04R1/326 , H04R2499/13
摘要： A controller for an autonomous vehicle receives audio signals from one or more microphones and identifies sounds. The controller further identifies an estimated location of the sound origin and the type of sound, i.e. whether the sound is a vehicle and/or the type of vehicle. The controller analyzes map data and attempts to identify a landmark within a tolerance from the estimated location. If a landmark is found corresponding to the estimated location and type of the sound origin, then the certainty is increased that the source of the sound is at that location and is that type of sound source. Collision avoidance is then performed with respect to the location of the sound origin and its type with the certainty as augmented using the map data. Collision avoidance may include automatically actuating brake, steering, and accelerator actuators in order to avoid the location of the sound origin.
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公开(公告)号：US20170200063A1
公开(公告)日：2017-07-13
申请号：US14995134
申请日：2016-01-13
CPC分类号： G06K9/6227 , G06K9/00818 , G06K9/4628 , G06K9/6274 , G06N3/0454 , G06N3/084 , G06T1/20 , G06T2207/30252
摘要： 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：US20220004807A1
公开(公告)日：2022-01-06
申请号：US17477282
申请日：2021-09-16
摘要： 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：US10373019B2
公开(公告)日：2019-08-06
申请号：US14995134
申请日：2016-01-13
摘要： 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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