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公开(公告)号：US20180106896A1
公开(公告)日：2018-04-19
申请号：US15262634
申请日：2016-09-12
申请人： Mohsen ROHANI , Song Zhang
发明人： Mohsen ROHANI , Song Zhang
CPC分类号： G01S13/426 , G01S13/87 , G01S13/89 , G01S13/931 , G01S2013/9382 , G01S2013/9389
摘要： A method and system for generating a three dimensional map of an environment based on information acquired by radar. The system includes a ground-based vehicle, and a scanning radar, or combination of radars, that scans the surrounding environment in one or more vertical planes or along azimuth angles. The radar may be an electrical beam steering and scanning radar or a combination electrical beam scanning radar and mechanical scanning radar. Dynamic objects within the environment may also be identified and removed with the remaining static objects being used to generate a three dimensional map of the surrounding environment and to perform localization within the three dimensional map.
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公开(公告)号：US20210081843A1
公开(公告)日：2021-03-18
申请号：US17022771
申请日：2020-09-16
摘要： Methods and systems for observation prediction in autonomous vehicles are described. A set of observations is received, including a current observation and one or more previous observations. Each observation includes a respective view of the environment and a vehicle state at each time step. A current action is received. A current-action embedded view is produced, the current-action embedded view representing an estimated change in vehicle state caused by the current action in a current view. A predicted view is generated from the current-action embedded view and the set of observations. The predicted view is re-centered. A predicted observation is fed back, including the re-centered predicted view and estimated change in vehicle state, to be included in the set of observations as input for multi-step training of the action-based prediction subsystem.
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公开(公告)号：US20200148215A1
公开(公告)日：2020-05-14
申请号：US16509045
申请日：2019-07-11
申请人： Nima MOHAJERIN , Mohsen ROHANI
发明人： Nima MOHAJERIN , Mohsen ROHANI
摘要： Methods and systems for generating a predicted occupancy grid map (OGM) over at least one future time step are described. The system include a first encoder for extracting OGM features from an input OGM in a current time step. The system also includes a recurrent neural network for generating a corrective term from at least the OGM features, wherein the corrective term represents predicted change to the input OGM, and wherein the corrective term is applied to the input OGM to generate a corrected OGM. The corrected OGM represents features corresponding to occupancy of the environment in a first future time step. The system also includes a classifier for converting the corrected OGM to the predicted OGM for the first future time step. The predicted OGM is fed back as input for performing generating a predicted OGM for a second future time step.
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公开(公告)号：US20220032935A1
公开(公告)日：2022-02-03
申请号：US16989776
申请日：2020-08-10
申请人： Jun LUO , Julian VILLELLA , Mohsen ROHANI , David RUSU , Montgomery ALBAN , Seyed Ershad BANIJAMALI
发明人： Jun LUO , Julian VILLELLA , Mohsen ROHANI , David RUSU , Montgomery ALBAN , Seyed Ershad BANIJAMALI
摘要： Method and system for controlling the behavior of an object. Behavior of the object is controlled during a first time period by using a first agent that applies a first behavior policy to map observations about the object and the environment in the first time period to a corresponding control action. Control is transitioned from the first agent to a second agent during a transition period following the first time period. Behavior of the object during a second time period following the transition period is controlled by using a second agent that applies a second behavior policy to map observations about the object and the environment in the second time period to a corresponding control action that is applied to the object. During transition the first agent applies the first behavior policy control the object and the second agent applies the second behavior policy to map observations about the object and the environment to corresponding control actions that are not applied to the object.
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公开(公告)号：US20210064040A1
公开(公告)日：2021-03-04
申请号：US16557368
申请日：2019-08-30
申请人： Peyman YADMELLAT , Mohsen ROHANI
发明人： Peyman YADMELLAT , Mohsen ROHANI
摘要： A processor-implemented method and system for determining a predictive occupancy grid map (OGM) for an autonomous vehicle are disclosed. The method includes: receiving a set of OGMs including a current predicted OGM and one or more future predicted OGMs, the current OGM associated with a current timestamp and each future predicted OGM associated with a future timestamp; generating a weight map associated with the current timestamp based on one or more kinodynamic parameters of the vehicle at the current time stamp, and one or more weight map associated with a future timestamp; generating a set of filtered predicted OGMs by filtering the current predicted OGM with the weight map associated the current timestamp and filtering each respective future predicted OGM associated with a future timestamp with the weight map associated with the respective future timestamp; and sending a single predicted OGM to a trajectory generator.
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6.发明公开公开(公告)号：US20230257003A1
公开(公告)日：2023-08-17
申请号：US18309150
申请日：2023-04-28
IPC分类号： B60W60/00 , G06V20/58 , G06V10/77 , G06V10/762
CPC分类号： B60W60/0027 , G06V20/58 , G06V10/77 , G06V10/7635 , B60W2554/4029 , B60W2554/4049 , B60W2554/4044
摘要： The present disclosure relates to methods and systems for spatiotemporal graph modelling of road users in observed frames of an environment in which an autonomous vehicle operates (i.e. a traffic scene), clustering of the road users into categories, and providing the spatiotemporal graph to a trained graphical convolutional neural network (GNN) to predict a future pedestrian action. The future pedestrian action can be: one of the pedestrian will cross a road and the pedestrian will not cross the road. The spatiotemporal graph includes a better understanding of the observed frames (i.e. traffic scene).
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公开(公告)号：US20220221866A1
公开(公告)日：2022-07-14
申请号：US17708886
申请日：2022-03-30
申请人： Peyman YADMELLAT , Mohsen ROHANI
发明人： Peyman YADMELLAT , Mohsen ROHANI
摘要： A processor-implemented method and system for determining a predictive occupancy grid map (OGM) for an autonomous vehicle are disclosed. The method includes: receiving a set of OGMs including a current predicted OGM and one or more future predicted OGMs, the current OGM associated with a current timestamp and each future predicted OGM associated with a future timestamp; generating a weight map associated with the current timestamp based on one or more kinodynamic parameters of the vehicle at the current time stamp, and one or more weight map associated with a future timestamp; generating a set of filtered predicted OGMs by filtering the current predicted OGM with the weight map associated the current timestamp and filtering each respective future predicted OGM associated with a future timestamp with the weight map associated with the respective future timestamp; and sending a single predicted OGM to a trajectory generator.
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8.发明申请公开(公告)号：US20210276598A1
公开(公告)日：2021-09-09
申请号：US16810552
申请日：2020-03-05
摘要： A system and method for path and/or motion planning and for training such a system are described. In one aspect, the method comprises generating a sequence of predicted occupancy grid maps (OGMs) for T-T1 time steps based on a sequence of OGMs for 0-T1 time steps, a reference map of an environment in which an autonomous vehicle is operating, and a trajectory. A cost volume is generated for the sequence of predicted OGMs. The cost volume comprises a plurality of cost maps for T-T1 time steps. Each cost map corresponds to a predicted OGM in the sequence of predicted OGMs and has the same dimensions as the corresponding predicted OGM. Each cost map comprises a plurality of cells.
Each cell in the cost map represents a cost of the cell in corresponding predicted OGM being occupied in accordance with a policy defined by a policy function.-
公开(公告)号：US20220156576A1
公开(公告)日：2022-05-19
申请号：US17097840
申请日：2020-11-13
申请人： Amir RASOULI , Mohsen ROHANI
发明人： Amir RASOULI , Mohsen ROHANI
IPC分类号： G06N3/08 , B60W60/00 , B60W30/095 , G06K9/32 , G06N3/04
摘要： Methods and systems for predicting behavior of a dynamic object of interest in an environment of a vehicle are described. Time series feature data are received, representing features of objects in the environment, including a dynamic object of interest. The feature data are categorized into one of a plurality of defined object categories. Each categorized set of data is encoded into a respective categorical representation that represents temporal change of features within the respective defined object category. The categorical representations are combined into a single shared representation. A categorical interaction representation is generated based on the single shared representation that represents contributions of temporal change in each defined object category to a final time step of the shared representation. The categorical interaction representation together with data representing dynamics of the objects in the environment and data representing a state of the vehicle are used to generate predicted data representing a predicted future behavior of the dynamic object of interest.
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公开(公告)号：US20220032951A1
公开(公告)日：2022-02-03
申请号：US16941505
申请日：2020-07-28
申请人： Jun LUO , Julian VILLELLA , Mohsen ROHANI , David RUSU , Montgomery ALBAN , Seyed Ershad BANIJAMALI
发明人： Jun LUO , Julian VILLELLA , Mohsen ROHANI , David RUSU , Montgomery ALBAN , Seyed Ershad BANIJAMALI
摘要： Method and system for controlling the behavior of an object. Behavior of the object is controlled during a first time period by using a first agent that applies a first behavior policy to map observations about a state of the object in the first time period to a corresponding control action. Control is transitioned from the first agent to a second agent during a transition period following the first time period. Behavior of the object during a second time period following the transition period is controlled by using a second agent that applies a second behavior policy to map observations about a current state of the object in the second time period to a corresponding control action that is applied to the object. During transition the first agent applies the first behavior policy control the object and the second agent applies the second behavior policy to map observations about the state of the object to corresponding control actions that are not applied to the object.
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