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公开(公告)号：US11772656B2
公开(公告)日：2023-10-03
申请号：US16923367
申请日：2020-07-08
CPC分类号： B60W40/02 , B60W60/005 , G06N20/00 , G06V10/751 , B60W2420/42 , B60W2555/20
摘要： A system includes a computer including a processor and a memory, the memory storing instructions executable by the processor to generate a synthetic image by adjusting respective color values of one or more pixels of a reference image based on a specified meteorological optical range from a vehicle sensor to simulated fog, and input the synthetic image to a machine learning program to train the machine learning program to identify a meteorological optical range from the vehicle sensor to actual fog.
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公开(公告)号：US20230237783A1
公开(公告)日：2023-07-27
申请号：US17584449
申请日：2022-01-26
发明人： Gaurab Banerjee , Vijay Nagasamy
IPC分类号： G06V10/80 , G01S17/86 , G01S15/86 , G01S13/86 , G01S17/89 , G01S15/89 , G01S13/89 , G06V10/82 , G06V20/58
CPC分类号： G06V10/803 , G01S17/86 , G01S15/86 , G01S13/865 , G01S13/867 , G01S13/862 , G01S17/89 , G01S15/89 , G01S13/89 , G06V10/82 , G06V20/58 , G01S17/931
摘要： A plurality of images can be acquired from a plurality of sensors and a plurality of flattened patches can be extracted from the plurality of images. An image location in the plurality of images and a sensor type token identifying a type of sensor used to acquire an image in the plurality of images from which the respective flattened patch was acquired can be added to each of the plurality of flattened patches. The flattened patches can be concatenated into a flat tensor and add a task token indicating a processing task to the flat tensor, wherein the flat tensor is a one-dimensional array that includes two or more types of data. The flat tensor can be input to a first deep neural network that includes a plurality of encoder layers and a plurality of decoder layers and outputs transformer output. The transformer output can be input to a second deep neural network that determines an object prediction indicated by the token and the object predictions can be output.
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公开(公告)号：US20200282910A1
公开(公告)日：2020-09-10
申请号：US16294540
申请日：2019-03-06
发明人： Vijay Nagasamy
摘要： A method for training an image-based trailer identification system comprises capturing a plurality of captured images in a field of view and identifying a detected trailer angle for a trailer in connection with a vehicle in each of the captured images. The method further comprises comparing the captured images and the corresponding trailer angles to a predetermined image set comprising a plurality of teaching trailer angles and identifying at least one required trailer angle of the teaching trailer angles that is not included in the captured images. Based on the captured images, a simulated angle image is generated. The simulated image comprises a depiction of the trailer in connection with the vehicle at the at least one required angle not included in the captured images. The method further comprises supplying the simulated angle image to the identification system for training.
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公开(公告)号：US12014508B2
公开(公告)日：2024-06-18
申请号：US17503596
申请日：2021-10-18
发明人： Zafar Iqbal , Hitha Revalla , Apurbaa Mallik , Gurjeet Singh , Vijay Nagasamy
摘要： A computer includes a processor and a memory storing instructions executable by the processor to receive image data from a camera, generate a depth map from the image data, detect an object in the image data, apply a bounding box circumscribing the object to the depth map, mask the depth map by setting depth values for pixels in the bounding box in the depth map to a depth value of a closest pixel in the bounding box, and determine a distance to the object based on the masked depth map. The closest pixel is closest to the camera of the pixels in the bounding box.
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公开(公告)号：US20220092356A1
公开(公告)日：2022-03-24
申请号：US17030857
申请日：2020-09-24
发明人： Vijay Nagasamy , Deepti Mahajan , Rohan Bhasin , Nikita Jaipuria , Gautham Sholingar , Vidya Nariyambut murali
摘要： A system, including a processor and a memory, the memory including instructions to be executed by the processor train a deep neural network based on plurality of real-world images, determine the accuracy of the deep neural network is below a threshold based on identifying one or more physical features by the deep neural network, including one or more object types, in the plurality of real-world images and generate a plurality of synthetic images based on the accuracy of the deep neural network is below a threshold based on identifying the one or more physical features using a photo-realistic image rendering software program and a generative adversarial network. The instructions can include further instructions to retrain the deep neural network based on the plurality of real-world images and the plurality of synthetic images and output the retrained deep neural network.
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公开(公告)号：US20220009498A1
公开(公告)日：2022-01-13
申请号：US16923367
申请日：2020-07-08
摘要： A system includes a computer including a processor and a memory, the memory storing instructions executable by the processor to, generate a synthetic image by adjusting respective color values of one or more pixels of a reference image based on a specified meteorological optical range from a vehicle sensor to simulated fog, and input the synthetic image to a machine learning program to train the machine learning program to identify a meteorological optical range from the vehicle sensor to actual fog.
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公开(公告)号：US20210350184A1
公开(公告)日：2021-11-11
申请号：US16867690
申请日：2020-05-06
摘要： A training system for a deep neural network and method of training is disclosed. The system and/or method may comprise: receiving, from an eye-tracking system associated with a sensor, an image frame captured while an operator is controlling a vehicle; receiving, from the eye-tracking system, eyeball gaze data corresponding to the image frame; and iteratively training the deep neural network to determine an object of interest depicted within the image frame based on the eyeball gaze data. The deep neural network generates at least one feature map and determine a proposed region corresponding to the object of interest within the at least one feature map based on the eyeball gaze data.
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公开(公告)号：US20210232812A1
公开(公告)日：2021-07-29
申请号：US16773339
申请日：2020-01-27
摘要： A training system for a neural network system and method of training is disclosed. The method may comprise: receiving, from a sensor, an image frame captured while an operator is controlling a vehicle; using an eye-tracking system associated with the sensor, monitoring the eyes of the operator to determine eyeball gaze data; determining, from the image frame, a plurality of pedestrians; and iteratively training the neural network system to determine, from among the plurality of pedestrians, the one or more target pedestrians using the eyeball gaze data and an answer dataset that is based on the eyeball gaze data, wherein the determined one or more target pedestrians have a relatively-higher probability of collision with the vehicle than a remainder of the plurality of pedestrians.
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公开(公告)号：US11975738B2
公开(公告)日：2024-05-07
申请号：US17337789
申请日：2021-06-03
IPC分类号： B60W60/00 , G06T3/4053 , G06T7/11 , G06T11/20 , G06V20/58
CPC分类号： B60W60/0011 , B60W60/0017 , G06T3/4053 , G06T7/11 , G06T11/20 , G06V20/58 , G06T2207/20081 , G06T2207/20084 , G06T2207/20132 , G06T2207/30261 , G06T2210/12
摘要： A first image can be acquired from a first sensor included in a vehicle and input to a deep neural network to determine a first bounding box for a first object. A second image can be acquired from the first sensor. Input latitudinal and longitudinal motion data from second sensors included in the vehicle corresponding to the time between inputting the first image and inputting the second image. A second bounding box can be determined by translating the first bounding box based on the latitudinal and longitudinal motion data. The second image can be cropped based on the second bounding box. The cropped second image can be input to the deep neural network to detect a second object. The first image, the first bounding box, the second image, and the second bounding box can be output.
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公开(公告)号：US11780445B2
公开(公告)日：2023-10-10
申请号：US16741425
申请日：2020-01-13
发明人： Ali Hassani , Aniruddh Ravindran , Vijay Nagasamy
CPC分类号： B60W40/08 , G06N3/044 , B60W2040/0872
摘要： Embodiments describe a vehicle configured with a brain machine interface (BMI) for a vehicle computing system to control vehicle functions using electrical impulses from motor cortex activity in a user's brain. A BMI training system trains the BMI device to interpret neural data generated by a motor cortex of a user and correlate the neural data to a vehicle control command associated with a neural gesture emulation function. A BMI system onboard the vehicle may receive a neural data feed of neural data from the user using the trained BMI device, determine, a user intention for a control instruction to control a vehicle infotainment system using the neural data feed, and perform an action based on the control instruction. The vehicle may further include a headrest configured as a Human Machine Interface (HMI) device that reads the electrical impulses without invasive electrode connectivity.
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