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公开(公告)号：US12084080B2
公开(公告)日：2024-09-10
申请号：US17896187
申请日：2022-08-26
Applicant: Toyota Research Institute, Inc.
Inventor： Guy Rosman , Daniel J. Brooks , Simon A. I. Stent , Tiffany Chen , Emily Sarah Sumner , Shabnam Hakimi , Jonathan DeCastro , Deepak Edakkattil Gopinath
CPC classification number: B60W50/14 , B25J9/161 , B25J9/163 , B25J11/008 , B60W40/09 , B60W50/0097 , B60W60/0015 , G06N3/04 , B60W2050/0083 , B60W2050/143 , B60W2050/146 , B60W2556/10
Abstract: Systems and methods for learning and managing robot user interfaces are disclosed herein. One embodiment generates, based on input data including information about past interactions of a particular user with a robot and with existing HMIs of the robot, a latent space using one or more encoder neural networks, wherein the latent space is a reduced-dimensionality representation of learned behavior and characteristics of the particular user, and uses the latent space as input to train a decoder neural network associated with (1) a new HMI distinct from the existing HMIs or (2) a particular HMI among the existing HMIs to alter operation of the particular HMI. The trained first decoder neural network is deployed in the robot to control, at least in part, operation of the new HMI or the particular HMI in accordance with the learned behavior and characteristics of the particular user.
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2.发明申请公开(公告)号：US20210302975A1
公开(公告)日：2021-09-30
申请号：US16830762
申请日：2020-03-26
Applicant: Toyota Research Institute, Inc.
Inventor： Stephen G. McGill, JR. , Guy Rosman , Xin Huang , Jonathan DeCastro , Luke S. Fletcher , John Joseph Leonard
IPC: G05D1/02 , G01S19/39 , G01S13/58 , G01S13/931 , G06N3/08
Abstract: Systems and methods for predicting a trajectory of a road agent are disclosed herein. One embodiment receives sensor data from one or more sensors; analyzes the sensor data to generate a predicted trajectory of the road agent, wherein the predicted trajectory includes a sequence of primitives, at least one primitive in the sequence of primitives having an associated duration that is determined in accordance with a dynamic timescale; and controls one or more aspects of the operation of an ego vehicle based, at least in part, on the predicted trajectory of the road agent.
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公开(公告)号：US11157756B2
公开(公告)日：2021-10-26
申请号：US16745560
申请日：2020-01-17
Applicant: Toyota Research Institute, Inc.
Inventor： Nikos Arechiga Gonzalez , Soonho Kong , Jonathan DeCastro , Sagar Behere , Dennis Park
Abstract: An artificial intelligence perception system for detecting one or more objects includes one or more processors, at least one sensor, and a memory device. The memory device includes an image capture module, an object identifying module, and a logical scaffold module. The image capture module and the object identifying module cause the one or more processors to obtain sensor information of a field of view from a sensor, identify an object within the sensor information, and determine at least one property of the object. The logical scaffold module causes the one or more processors to determine, by a logical scaffold, when the at least one property of the object as determined by the object identifying module is one of a true condition or a false condition.
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公开(公告)号：US11072342B2
公开(公告)日：2021-07-27
申请号：US16279857
申请日：2019-02-19
Applicant: TOYOTA RESEARCH INSTITUTE, INC.
Inventor： Soonho Kong , Jonathan DeCastro , Nikos Arechiga , Frank Permenter
Abstract: A method for controlling an autonomous vehicle includes navigating the autonomous vehicle based on a set of rules. The method also includes identifying an abnormality in a current driving situation. The method further includes prompting a passenger of the autonomous vehicle to interact with a driver of a first vehicle in response to identifying the abnormality. The method still further includes controlling the autonomous vehicle to violate one or more rules of the set of rules in response to an indication of a successful interaction with the driver.
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公开(公告)号：US20250121832A1
公开(公告)日：2025-04-17
申请号：US18603887
申请日：2024-03-13
Applicant: Toyota Research Institute, Inc.
Inventor： Jean Marcel dos Reis Costa , Guy Rosman , Deepak Edakkattil Gopinath , Emily Sumner , Thomas Balch , Jonathan DeCastro , Andrew Michael Silva , Laporsha Trinati Dees
Abstract: Systems, methods, and other embodiments described herein relate to integrating human decision-making into a model-based system. In one embodiment, a method includes acquiring sensor data, including driver data about a driver of a vehicle and driving data about the vehicle and a surrounding environment of the vehicle. The method includes encoding, using a world encoder, the sensor data into a latent representation. The method includes determining human decision- making characteristics according to the latent representation. The method includes generating a control signal for providing shared control of the vehicle according to the human decision-making characteristics and the latent representation.
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Patent Agency Ranking
公开(公告)号：US20240391502A1
公开(公告)日：2024-11-28
申请号：US18483479
申请日：2023-10-09
Inventor： Guy Rosman , Justin Lidard , Oswin So , Yanxia Zhang , Paul M. Drews , Jonathan DeCastro , Xiongyi Cui , Yen-Ling Kuo , John J. Leonard , Avinash Balachandran , Naomi Ehrich Leonard
IPC: B60W60/00
Abstract: Systems and methods are provided trajectory prediction that leverages game-theory to improve coverage of multi-modal predictions. Examples of the systems and methods include obtaining training data including first trajectories for a first plurality of agent devices and first map information of a first environment for a past time horizon and applying the training data to a game-theoretic mode-finding algorithm to generate a mode-finding model for each agent device that predicts modes of the first trajectories. A trajectory prediction model can be trained on the predicted modes as a coverage loss term between predicted modes. Future trajectories can be predicted for a second plurality of agent devices based on applying observed data to the trajectory prediction model. A control signal can then be generated to effectuate an autonomous driving command on an agent device of the second plurality of agent devices based on the predicted future trajectories.
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公开(公告)号：US20250121845A1
公开(公告)日：2025-04-17
申请号：US18613419
申请日：2024-03-22
Applicant: Toyota Research Institute , Inc.
Inventor： Guy Rosman , Jean Marcel dos Reis Costa , Hiroshi Yasuda , Deepak Edakkattil Gopinath , Jonathan DeCastro , Tiffany L. Chen , Avinash Balachandran
IPC: B60W50/14
Abstract: Systems, methods, and other embodiments described herein relate to stylizing messages within a vehicle according to an occupant and a current context. In one embodiment, a method includes determining a style for presenting messages associated with an occupant of a vehicle according to a context defined in relation to an occupant and an environment of the vehicle. The method includes generating a message according to the style for the occupant. The method includes providing the message to the occupant.
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公开(公告)号：US20240249637A1
公开(公告)日：2024-07-25
申请号：US18156591
申请日：2023-01-19
Applicant: Toyota Research Institute, Inc.
Inventor： Simon Stent , Andrew P. Best , Shabnam Hakimi , Guy Rosman , Emily S. Sumner , Jonathan DeCastro
IPC: G09B9/05
CPC classification number: G09B9/05
Abstract: A driving simulator may include a controller programmed to simulate operation of a vehicle being driven by a driver, the vehicle including assistive driving technology, receive driver data associated with the driver, determine whether the driver is distracted based on the driver data, and upon determination that the driver is distracted, simulate a particular driving event.
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公开(公告)号：US10882522B2
公开(公告)日：2021-01-05
申请号：US16130458
申请日：2018-09-13
Applicant: Toyota Research Institute, Inc.
Inventor： Guy Rosman , Jonathan DeCastro , Nikos Arechiga Gonzalez , John Joseph Leonard , Luke S. Fletcher , Daniel Stonier
IPC: B60W30/095 , B60W30/09 , G06K9/00 , G05D1/00 , G06N7/00
Abstract: System, methods, and other embodiments described herein relate to modeling dynamic agents in a surrounding environment of an ego vehicle. In one embodiment, a method includes, in response to receiving sensor data including present observations of a road agent of the dynamic agents in the surrounding environment, identifying previous observations of the road agent from an electronic data store. The method includes estimating a future state of the road agent using at least the present observations and the previous observations of the road agent to compute the future state according to a probabilistic model comprised of a transition model that accounts for dynamic behaviors of the road agent to characterize transitions between states, and an agent model that accounts for actions of the road agent. The method includes controlling one or more vehicle systems of the ego vehicle according to the future state of the road agent.
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公开(公告)号：US20240010218A1
公开(公告)日：2024-01-11
申请号：US17896187
申请日：2022-08-26
Applicant: Toyota Research Institute, Inc.
Inventor： Guy Rosman , Daniel J. Brooks , Simon A.I. Stent , Tiffany Chen , Emily Sarah Sumner , Shabnam Hakimi , Jonathan DeCastro , Deepak Edakkattil Gopinath
CPC classification number: B60W50/14 , B60W40/09 , B60W50/0097 , B60W60/0015 , B25J9/163 , B25J9/161 , B25J11/008 , G06N3/04 , B60W2050/0083 , B60W2050/143 , B60W2050/146 , B60W2556/10
Abstract: Systems and methods for learning and managing robot user interfaces are disclosed herein. One embodiment generates, based on input data including information about past interactions of a particular user with a robot and with existing HMIs of the robot, a latent space using one or more encoder neural networks, wherein the latent space is a reduced-dimensionality representation of learned behavior and characteristics of the particular user, and uses the latent space as input to train a decoder neural network associated with (1) a new HMI distinct from the existing HMIs or (2) a particular HMI among the existing HMIs to alter operation of the particular HMI. The trained first decoder neural network is deployed in the robot to control, at least in part, operation of the new HMI or the particular HMI in accordance with the learned behavior and characteristics of the particular user.
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