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公开(公告)号：US20220392083A1
公开(公告)日：2022-12-08
申请号：US17489237
申请日：2021-09-29
摘要： Systems and methods described herein relate to jointly training a machine-learning-based monocular optical flow, depth, and scene flow estimator. One embodiment processes a pair of temporally adjacent monocular image frames using a first neural network structure to produce a first optical flow estimate; processes the pair of temporally adjacent monocular image frames using a second neural network structure to produce an estimated depth map and an estimated scene flow; processes the estimated depth map and the estimated scene flow using the second neural network structure to produce a second optical flow estimate; and imposes a consistency loss between the first optical flow estimate and the second optical flow estimate that minimizes a difference between the first optical flow estimate and the second optical flow estimate to improve performance of the first neural network structure in estimating optical flow and the second neural network structure in estimating depth and scene flow.
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公开(公告)号：US11514685B2
公开(公告)日：2022-11-29
申请号：US17177516
申请日：2021-02-17
发明人： Kun-Hsin Chen , Peiyan Gong , Sudeep Pillai , Arjun Bhargava , Shunsho Kaku , Hai Jin , Kuan-Hui Lee
摘要： Systems, methods, computer-readable media, techniques, and methodologies are disclosed for performing end-to-end, learning-based keypoint detection and association. A scene graph of a signalized intersection is constructed from an input image of the intersection. The scene graph includes detected keypoints and linkages identified between the keypoints. The scene graph can be used along with a vehicle's localization information to identify which keypoint that represents a traffic signal is associated with the vehicle's current travel lane. An appropriate vehicle action may then be determined based on a transition state of the traffic signal keypoint and trajectory information for the vehicle. A control signal indicative of this vehicle action may then be output to cause an autonomous vehicle, for example, to implement the appropriate vehicle action.
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公开(公告)号：US11475628B2
公开(公告)日：2022-10-18
申请号：US17147049
申请日：2021-01-12
发明人： Arjun Bhargava , Sudeep Pillai , Kuan-Hui Lee , Kun-Hsin Chen
摘要： A method for 3D object modeling includes linking 2D semantic keypoints of an object within a video stream into a 2D structured object geometry. The method includes inputting, to a neural network, the object to generate a 2D NOCS image and a shape vector, the shape vector being mapped to a continuously traversable coordinate shape. The method includes applying a differentiable shape renderer to the SDF shape and the 2D NOCS image to render a shape of the object corresponding to a 3D object model in the continuously traversable coordinate shape space. The method includes lifting the linked, 2D semantic keypoints of the 2D structured object geometry to a 3D structured object geometry. The method includes geometrically and projectively aligning the 3D object model, the 3D structured object geometry, and the rendered shape to form a rendered object. The method includes generating 3D bounding boxes from the rendered object.
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公开(公告)号：US11361557B2
公开(公告)日：2022-06-14
申请号：US16389255
申请日：2019-04-19
摘要： A method for performing vehicle taillight recognition is described. The method includes extracting spatial features from a sequence of images of a real-world traffic scene during operation of an ego vehicle. The method includes selectively focusing a convolutional neural network (CNN) of a CNN-long short-term memory (CNN-LSTM) framework on a selected region of the sequence of images according to a spatial attention model for a vehicle taillight recognition task. The method includes selecting, by an LSTM network of the CNN-LSTM framework, frames within the selected region of the sequence of images according to a temporal attention model for the vehicle taillight recognition task. The method includes inferring, according to the selected frames within the selected region of the sequence of images, an intent of an ado vehicle according to a taillight state. The method includes planning a trajectory of the ego vehicle from the intent inferred from the ado vehicle.
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公开(公告)号：US20210245744A1
公开(公告)日：2021-08-12
申请号：US16787523
申请日：2020-02-11
IPC分类号： B60W30/095 , G06K9/00 , G08G1/16 , G06N7/00 , G06N3/02
摘要： A system and related method for predicting movement of a plurality of pedestrians may include one or more processors and a memory. The memory includes an initial trajectory module, an exit point prediction module, a path planning module, and an adjustment module. The modules include instructions that when executed by the one or more processors cause the one or more processors to obtain trajectories of the plurality of pedestrians, predict future exit points for the plurality of pedestrians from a scene based on the trajectories of the plurality of pedestrians, determine trajectory paths of the plurality of pedestrians based on the future exit points and at least one scene element of a map, and adjust the trajectory paths based on at least one predicted interaction between at least two of the plurality of pedestrians.
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公开(公告)号：US10466361B2
公开(公告)日：2019-11-05
申请号：US15601433
申请日：2017-05-22
IPC分类号： G01S17/93 , G01S17/02 , G01S5/02 , G01S13/72 , G01S13/86 , G01S13/93 , G01S15/93 , G01S15/02 , G01S13/87
摘要： System, methods, and other embodiments described herein relate to associating disparate tracks from multiple sensor inputs for observed objects. In one embodiment, a method includes, in response to receiving a first input from a first sensor and a second input from a second sensor, generating the disparate tracks including first sensor tracks and second sensor tracks for the observed objects that correspond to the first input and the second input. The method includes identifying correlations between the first sensor tracks and the second sensor tracks by computing association likelihoods between the first tracks and the second tracks within a permutation matrix according to an objective cost function. The method includes controlling a vehicle according to the correlations.
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公开(公告)号：US10409288B2
公开(公告)日：2019-09-10
申请号：US15585878
申请日：2017-05-03
发明人： Yusuke Kanzawa , Kuan-Hui Lee
摘要： System, methods, and other embodiments described herein relate to locating an object within an image and projecting the object into a map. In one embodiment, a method includes, responsive to identifying a nearby vehicle in an image of an external environment surrounding a scanning vehicle, determining a relative location of the nearby vehicle in relation to the scanning vehicle using the image. The method includes projecting the nearby vehicle into a map according to at least the relative location determined from the image. The method includes controlling the scanning vehicle according to the map.
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8.发明申请公开(公告)号：US20180267544A1
公开(公告)日：2018-09-20
申请号：US15601433
申请日：2017-05-22
CPC分类号： G01S17/936 , G01S5/0263 , G01S5/0278 , G01S13/726 , G01S13/865 , G01S13/867 , G01S13/87 , G01S13/931 , G01S15/025 , G01S15/931 , G01S17/023 , G01S2013/9342 , G01S2013/9346 , G01S2013/935 , G01S2013/9364 , G01S2013/9367
摘要： System, methods, and other embodiments described herein relate to associating disparate tracks from multiple sensor inputs for observed objects. In one embodiment, a method includes, in response to receiving a first input from a first sensor and a second input from a second sensor, generating the disparate tracks including first sensor tracks and second sensor tracks for the observed objects that correspond to the first input and the second input. The method includes identifying correlations between the first sensor tracks and the second sensor tracks by computing association likelihoods between the first tracks and the second tracks within a permutation matrix according to an objective cost function. The method includes controlling a vehicle according to the correlations.
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公开(公告)号：US12080161B2
公开(公告)日：2024-09-03
申请号：US17732376
申请日：2022-04-28
IPC分类号： G08G1/01
CPC分类号： G08G1/0125 , G08G1/0141
摘要： A method for vehicle prediction, planning, and control is described. The method includes separately encoding traffic state information at an intersection into corresponding traffic state latent spaces. The method also includes aggregating the corresponding traffic state latent spaces to form a generalized traffic geometry latent space. The method further includes interpreting the generalized traffic geometry latent space to form a traffic flow map including current and future vehicle trajectories. The method also includes decoding the generalized traffic geometry latent space to predict a vehicle behavior according to the traffic flow map based on the current and future vehicle trajectories.
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10.发明授权公开(公告)号：US12039438B2
公开(公告)日：2024-07-16
申请号：US17112292
申请日：2020-12-04
发明人： Boris Ivanovic , Kuan-Hui Lee , Jie Li , Adrien David Gaidon , Pavel Tokmakov
CPC分类号： G06N3/08 , B60W30/0956 , G06N3/044 , B60W60/0027 , B60W2554/4044 , G05D1/0214
摘要： Systems, methods, and other embodiments described herein relate to improving trajectory forecasting in a device. In one embodiment, a method includes, in response to receiving sensor data about a surrounding environment of the device, identifying an object from the sensor data that is present in the surrounding environment. The method includes determining category probabilities for the object, the category probabilities indicating semantic classes for classifying the object and probabilities that the object belongs to the semantic classes. The method includes forecasting trajectories for the object based, at least in part, on the category probabilities and the sensor data. The method includes controlling the device according to the trajectories.
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