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公开(公告)号：US11763497B2
公开(公告)日：2023-09-19
申请号：US17505344
申请日：2021-10-19
发明人： Yongkang Liu , Xuewei Qi , Kentaro Oguchi
CPC分类号： G06T11/00 , G06T3/0093
摘要： A method for generating a dataset is provided. The method includes generating, within a simulated environment, a simulated image including one or more distortions, the simulated image includes a plurality of vehicles, generating vehicle image patches and ground truth from the simulated image, performing, using a style transfer module, a style-transfer operation on the vehicle image patches, combining the vehicle image patches, on which the style-transfer operation is performed, with a background image of a real-world location, and generating a dataset based on the ground truth and the combination of the vehicle image patches and the background image.
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公开(公告)号：US20230098141A1
公开(公告)日：2023-03-30
申请号：US17490354
申请日：2021-09-30
发明人： Xuewei Qi , Rui Guo , Prashant Tiwari , Chang-Heng Wang , Takayuki Shimizu
摘要： System, methods, and embodiments described herein relate to dynamically generating a wide field-of-view three-dimensional pseudo point cloud of an environment around a vehicle. A disclosed method may include capturing, via a camera, a first view in a first image, determining a first depth map based on the first image, obtaining, from an external system, a second image of a second view that overlaps the first view and a second depth map based on the second image, inputting the first image and second image into a self-supervised homograph network that is trained to output a homographic transformation matrix between the first image and the second image, and generating a three-dimensional pseudo point cloud that combines the first depth map and the second depth map based on the homographic transformation matrix.
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公开(公告)号：US11548515B2
公开(公告)日：2023-01-10
申请号：US17155913
申请日：2021-01-22
发明人： Seyhan Ucar , Xuewei Qi , Kentaro Oguchi
摘要： Systems and methods for managing driver habits are disclosed herein. One embodiment learns undesirable driving habits of a driver over time as the driver operates a vehicle; identifies, for each learned undesirable driving habit, one or more situational triggers associated with that undesirable driving habit; receives information from one or more of vehicle sensors and one or more external sources; predicts that the driver will engage in a particular undesirable driving habit; and carries out one or more of the following avoidance strategies to assist the driver in refraining from engaging in the particular undesirable driving habit: communicating one or more speed advisories to the driver; suggesting an alternate route to the driver; and presenting the driver with one or more of a coupon, an offer, and a discount at a place of business to encourage the driver to take a break by stopping at the place of business.
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4.发明公开公开(公告)号：US20230367013A1
公开(公告)日：2023-11-16
申请号：US17744842
申请日：2022-05-16
IPC分类号： G01S17/58 , G01S17/89 , G01S7/4861 , G06T17/05
CPC分类号： G01S17/58 , G01S7/4861 , G01S17/89 , G06T17/05 , G06T2207/10028
摘要： System, methods, and other embodiments described herein relate to cooperative perception. In one embodiment, a method includes computing, at a first timestep, a base relative pose between an ego vehicle and a remote vehicle based upon respective point clouds of the ego vehicle and the remote vehicle. The method includes computing, at a second timestep, a relative pose between the ego vehicle and the remote vehicle based upon the base relative pose, a first temporal relative pose of the ego vehicle, and a second temporal relative pose received from the remote vehicle. The method includes generating a combined point cloud based upon a first point cloud of the ego vehicle, a second point cloud received from the remote vehicle, and the relative pose.
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公开(公告)号：US20230116442A1
公开(公告)日：2023-04-13
申请号：US17500447
申请日：2021-10-13
发明人： Xuewei Qi , Kentaro Oguchi , Yongkang Liu
摘要： In accordance with one embodiment of the present disclosure, method includes obtaining multi-level environment data corresponding to a plurality of driving environment levels, encoding the multi-level environment data at each level, extracting features from the multi-level environment data at each encoded level, fusing the extracted features from each encoded level with a spatial-temporal attention framework to generate a fused information embedding, and decoding the fused information embedding to predict driving environment information at one or more driving environment levels.
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公开(公告)号：US20220398757A1
公开(公告)日：2022-12-15
申请号：US17342853
申请日：2021-06-09
发明人： Rui Guo , Xuewei Qi , Kentaro Oguchi , Kareem Metwaly
摘要： System, methods, and other embodiments described herein relate to improving depth prediction for objects within a low-light image using a style model. In one embodiment, a method includes encoding, by a style model, an input image to identify content information. The method also includes decoding, by the style model, the content information into an albedo component and a shading component. The method also includes generating, by the style model, a synthetic image using the albedo component and the shading component. The method also includes providing the synthetic image to a depth model.
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公开(公告)号：US20220388522A1
公开(公告)日：2022-12-08
申请号：US17339063
申请日：2021-06-04
发明人： Xuewei Qi , Kentaro Oguchi , Yongkang Liu
摘要： A system for learning optimal driving behavior for autonomous vehicles comprises a deep neural network, a first stage training module, and a second stage training module. The deep neural network comprises a feature learning network configured to receive sensor data from a vehicle as input and output spatial temporal feature embeddings and a decision action network configured to receive the spatial temporal feature embeddings as input and output an optimal driving policy for the vehicle. The first training stage module is configured to, during a first training stage, train the feature learning network using object detection loss. The second stage training module is configured to, during a second training stage, train the decision action network using reinforcement learning.
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公开(公告)号：US11935254B2
公开(公告)日：2024-03-19
申请号：US17342853
申请日：2021-06-09
发明人： Rui Guo , Xuewei Qi , Kentaro Oguchi , Kareem Metwaly
CPC分类号： G06T7/50 , G06N3/045 , G06T7/70 , G06T2207/10028 , G06T2207/20081 , G06T2207/20084
摘要： System, methods, and other embodiments described herein relate to improving depth prediction for objects within a low-light image using a style model. In one embodiment, a method includes encoding, by a style model, an input image to identify content information. The method also includes decoding, by the style model, the content information into an albedo component and a shading component. The method also includes generating, by the style model, a synthetic image using the albedo component and the shading component. The method also includes providing the synthetic image to a depth model.
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公开(公告)号：US20230274641A1
公开(公告)日：2023-08-31
申请号：US17681092
申请日：2022-02-25
发明人： Xuewei Qi , Kentaro Oguchi , Yongkang Liu , Emrah Akin Sisbot
IPC分类号： G08G1/0967 , G08G1/01
CPC分类号： G08G1/096775 , G08G1/0129
摘要： Systems, methods, and other embodiments described herein relate to improving the performance of a device in different geographic locations by using transfer learning to provide a customized learning model for the different locations. In one embodiment, a method includes receiving segments of a model from separate members in a geographic hierarchy and assembling the segments into the model. The segments include at least a first segment, a second segment, and a third segment. The method includes processing sensor data using the model to provide an output for assisting a device.
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公开(公告)号：US20230118817A1
公开(公告)日：2023-04-20
申请号：US17505344
申请日：2021-10-19
发明人： Yongkang Liu , Xuewei Qi , Kentaro Oguchi
摘要： A method for generating a dataset is provided. The method includes generating, within a simulated environment, a simulated image including one or more distortions, the simulated image includes a plurality of vehicles, generating vehicle image patches and ground truth from the simulated image, performing, using a style transfer module, a style-transfer operation on the vehicle image patches, combining the vehicle image patches, on which the style-transfer operation is performed, with a background image of a real-world location, and generating a dataset based on the ground truth and the combination of the vehicle image patches and the background image.
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