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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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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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公开(公告)号：US20230242142A1
公开(公告)日：2023-08-03
申请号：US17590707
申请日：2022-02-01
申请人： Weize ZHANG , Peyman YADMELLAT
发明人： Weize ZHANG , Peyman YADMELLAT
IPC分类号： B60W60/00 , B60W40/107 , B60W40/109
CPC分类号： B60W60/001 , B60W40/107 , B60W40/109 , B60W2554/4041
摘要： Systems, methods and computer-readable media for spatio-temporal motion planning, including: receiving data defining a drivable area within a spatio-temporal space; generating a cost function to be minimized that includes one or more upper-bound cost terms, each upper-bound cost term approximating the upper-bound of a cost term for the trajectory for a spatial frame of the spatio-temporal space, each upper-bound cost term being expressed as one or more longitudinal-temporal cost terms and one or more lateral-temporal cost terms for the trajectory; and further cost terms for the trajectory for the spatio-temporal frames; computing, based on the constraints and cost function, a planned trajectory through the drivable area, the planned trajectory directory being optimized with respect to both spatial frame and the spatio-temporal frames. Optimizing the planned trajectory in the spatial frame in addition to the spatio-temporal frames can enable planning objectives for the spatial frame to be optimized, for example maintaining closeness to a reference path and minimizing lateral acceleration and directional oscillation.
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公开(公告)号：US20230082654A1
公开(公告)日：2023-03-16
申请号：US17944943
申请日：2022-09-14
申请人： Kasra REZAEE , Peyman YADMELLAT
发明人： Kasra REZAEE , Peyman YADMELLAT
摘要： Systems, methods and computer-readable media for training a constraint model to indicate a validity of a planned activity, including training a distribution model and then training a constraint model by generating, using the constraint model, a respective constraint prediction for proposed activity samples; generating, using the trained distribution model, a respective distribution prediction for at least some of the proposed activity samples indicated by the constraint model as being valid proposed activity samples; adding, to a set of adversarial samples, the proposed activity samples that are indicated both by the constraint model as being valid proposed activity samples and by the distribution model as being as being out-of-distribution; and updating the constraint model based on the set of adversarial samples.
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公开(公告)号：US20210004647A1
公开(公告)日：2021-01-07
申请号：US16920598
申请日：2020-07-03
摘要： Methods and systems of training RL agent for autonomous operation of a vehicle are described. The RL agent is trained using uniformly sampled training samples and learning a policy. After the RL agent has achieved a predetermined performance goal, data is collected including a sequence of sampled states, and for each sequence of sampled states, agent parameters, and an indication of failure of the RL agent for the sequence. A failure predictor is trained, using samples from the collected data, to predict a probability of failure of the RL agent for a given sequence of states. Sequences of states are collected by simulating interaction of the vehicle with the environment. Based on a probability of failure outputted by the failure predictor, a sequence of states is selected. The RL agent is further trained based on the selected sequence of states.
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公开(公告)号：US20220219726A1
公开(公告)日：2022-07-14
申请号：US17147256
申请日：2021-01-12
申请人： Peyman YADMELLAT , Kasra REZAEE
发明人： Peyman YADMELLAT , Kasra REZAEE
摘要： Systems, methods and computer-readable media for selecting a trajectory for an autonomous vehicle are disclosed. A trajectory evaluator may be used to sort candidate trajectories according to criteria associated with a plurality of objectives. Criterion satisfaction data is generated for each candidate trajectory for each objective, indicating whether the criterion is likely to be satisfied by the candidate trajectory. Criteria are ranked by priority, which determines the sorting order. Candidate trajectories that satisfy similar criteria may be grouped into a common category, and candidate trajectories within each category may be sorted according to a cost function. The cost function may be a single universal cost function applied to all categories, or may be selected from a plurality of cost functions based on the criterion satisfaction data of the trajectories being sorted, such as a cost function specific to each category.
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公开(公告)号：US20220135068A1
公开(公告)日：2022-05-05
申请号：US17515522
申请日：2021-10-31
申请人： Han HU , Peyman YADMELLAT
发明人： Han HU , Peyman YADMELLAT
IPC分类号： B60W60/00
摘要： The present disclosure relates to methods and systems for generating a channel for use as a spatio-temporal constraint for motion planning for an autonomous vehicle. The method generates a triangulation mesh for a space in an environment. The triangulation mesh includes a plurality of nodes, each represents a geographic location in the space. Some of the nodes correspond to dynamic objects. Based on the triangulation mesh, a candidate channel extending from a starting location to a target location of the autonomous vehicle is generated. Further, the method predicts a time and location of a future triangulation mesh topology event caused by at least one of the nodes that will impact the candidate channel. A valid channel segment for the channel is selected, which extends from the starting location to a channel segment end location preceding the predicted location of the future triangulation mesh topology event. The channel improves the validity of motion planning for a longer time period.
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公开(公告)号：US20220035375A1
公开(公告)日：2022-02-03
申请号：US16940807
申请日：2020-07-28
申请人： Kasra REZAEE , Peyman YADMELLAT
发明人： Kasra REZAEE , Peyman YADMELLAT
摘要： Methods and systems for training a motion planner for an autonomous vehicle are described. A trajectory evaluator agent of the motion planner receives state data defining a current state of the autonomous vehicle and an environment at a current time step. Based on the current state, a trajectory is selected. A reward is calculated based on performance of the selected trajectory in the current state. State data is received for a next state of the autonomous vehicle and the environment at a next time step. Parameters of the trajectory evaluator agent are updated based on the current state, selected trajectory, computed reward and next state. The parameters of the trajectory evaluator agent are updated to assign an evaluation value for the selected trajectory that reflects the calculated reward and expected performance of the selected trajectory in the future states.
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