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公开(公告)号：US20220204030A1
公开(公告)日：2022-06-30
申请号：US17139105
申请日：2020-12-31
申请人： Toyota Research Institute, Inc. , The Board of Trustees of the Leland Stanford Junior University
IPC分类号： B60W60/00 , B60W30/095 , B60W30/09 , G06K9/00
摘要： System, methods, and other embodiments described herein relate to improving controls in a device according to risk. In one embodiment, a method includes, in response to receiving sensor data about a surrounding environment of the device, identifying objects from the sensor data that are present in the surrounding environment. The method includes generating a control sequence for controlling the device according to a risk-sensitivity parameter to navigate toward a destination while considering risk associated with encountering the objects defined by the risk-sensitivity parameter. The method includes controlling the device according to the control sequence.
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公开(公告)号：US12061480B2
公开(公告)日：2024-08-13
申请号：US17228165
申请日：2021-04-12
申请人： Toyota Research Institute, Inc. , The Board of Trustees of the Leland Stanford Junior University
发明人： Boris Ivanovic , Amine Elhafsi , Guy Rosman , Adrien David Gaidon , Marco Pavone
IPC分类号： G05D1/00 , G06F18/214 , G06N3/08
CPC分类号： G05D1/0221 , G05D1/0088 , G05D1/0219 , G05D1/0223 , G05D1/024 , G06F18/214 , G06N3/08
摘要： A mobile robot can be caused to move according to a planned trajectory. The mobile robot can be a vehicle. Information about agents in an environment of the mobile robot can be received from sensors. At a first time, a spatiotemporal graph can be produced. The spatiotemporal graph can represent relationships among the agents in the environment. The mobile robot can be one of the agents in the environment. Information from the spatiotemporal graph can be input to neural networks to produce information for a mixture of affine time-varying systems. The mixture of affine time-varying systems can represent an evolution of agent states of the agents. Using the mixture of affine time-varying systems and information associated with the first time, a prediction of the agent states at a second time can be calculated. The mobile robot can be caused to move according to the planned trajectory determined from the prediction.
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公开(公告)号：US11807267B2
公开(公告)日：2023-11-07
申请号：US17139105
申请日：2020-12-31
申请人： Toyota Research Institute, Inc. , The Board of Trustees of the Leland Stanford Junior University
IPC分类号： B60W60/00 , B60W30/09 , B60W30/095 , G06V20/58
CPC分类号： B60W60/0011 , B60W30/09 , B60W30/0956 , B60W60/00274 , G06V20/58 , B60W2554/80
摘要： Systems, methods, and other embodiments described herein relate to improving controls in a device according to risk. In one embodiment, a method includes, in response to receiving sensor data about a surrounding environment of the device, identifying objects from the sensor data that are present in the surrounding environment. The method includes generating a control sequence for controlling the device according to a risk-sensitivity parameter to navigate toward a destination while considering risk associated with encountering the objects defined by the risk-sensitivity parameter. The method includes controlling the device according to the control sequence.
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公开(公告)号：US20220066460A1
公开(公告)日：2022-03-03
申请号：US17228165
申请日：2021-04-12
申请人： Toyota Research Institute, Inc. , The Board of Trustees of the Leland Stanford Junior University
发明人： Boris Ivanovic , Amine Elhafsi , Guy Rosman , Adrien David Gaidon , Marco Pavone
摘要： A mobile robot can be caused to move according to a planned trajectory. The mobile robot can be a vehicle. Information about agents in an environment of the mobile robot can be received from sensors. At a first time, a spatiotemporal graph can be produced. The spatiotemporal graph can represent relationships among the agents in the environment. The mobile robot can be one of the agents in the environment. Information from the spatiotemporal graph can be input to neural networks to produce information for a mixture of affine time-varying systems. The mixture of affine time-varying systems can represent an evolution of agent states of the agents. Using the mixture of affine time-varying systems and information associated with the first time, a prediction of the agent states at a second time can be calculated. The mobile robot can be caused to move according to the planned trajectory determined from the prediction.
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公开(公告)号：US11887317B2
公开(公告)日：2024-01-30
申请号：US17460776
申请日：2021-08-30
IPC分类号： G06T7/277 , G06T7/70 , B60W10/18 , B60W10/20 , B60W10/04 , G08G1/01 , B60W40/04 , G06N3/044 , G06N3/045
CPC分类号： G06T7/277 , B60W10/04 , B60W10/18 , B60W10/20 , B60W40/04 , G06N3/044 , G06N3/045 , G06T7/70 , G08G1/0112 , G08G1/0116 , G06T2207/20081 , G06T2207/20084 , G06T2207/30252
摘要： A plurality of agent locations can be determined at a plurality of time steps by inputting a plurality of images to a perception algorithm that inputs the plurality of images and outputs agent labels and the agent locations. A plurality of first uncertainties corresponding to the agent locations can be determined at the plurality of time steps by inputting the plurality of agent locations to a filter algorithm that inputs the agent locations and outputs the plurality of first uncertainties corresponding to the plurality of agent locations. A plurality of predicted agent trajectories and potential trajectories corresponding to the predicted agent trajectories can be determined by inputting the plurality of agent locations at the plurality of time steps and the first uncertainties corresponding to the agent locations at the plurality of time steps to a variational autoencoder. The plurality of predicted agent trajectories and the potential trajectories corresponding to the predicted agent trajectories can be output.
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公开(公告)号：US20230074293A1
公开(公告)日：2023-03-09
申请号：US17460776
申请日：2021-08-30
摘要： A plurality of agent locations can be determined at a plurality of time steps by inputting a plurality of images to a perception algorithm that inputs the plurality of images and outputs agent labels and the agent locations. A plurality of first uncertainties corresponding to the agent locations can be determined at the plurality of time steps by inputting the plurality of agent locations to a filter algorithm that inputs the agent locations and outputs the plurality of first uncertainties corresponding to the plurality of agent locations. A plurality of predicted agent trajectories and potential trajectories corresponding to the predicted agent trajectories can be determined by inputting the plurality of agent locations at the plurality of time steps and the first uncertainties corresponding to the agent locations at the plurality of time steps to a variational autoencoder. The plurality of predicted agent trajectories and the potential trajectories corresponding to the predicted agent trajectories can be output.
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7.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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8.发明申请公开(公告)号：US20220180170A1
公开(公告)日：2022-06-09
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
IPC分类号： G06N3/08 , G06N3/04 , B60W30/095
摘要： System, 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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