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11.发明公开公开(公告)号：US20230280753A1
公开(公告)日：2023-09-07
申请号：US18118696
申请日：2023-03-07
Applicant: Waymo LLC
Inventor： Benjamin James Caine , Khaled Refaat , Benjamin Sapp , Scott Morgan Ettinger , Wei Chai , Rebecca Dawn Roelofs , Liting Sun
IPC: G05D1/02 , G06V20/54 , G06V10/82 , G06V10/774
CPC classification number: G05D1/0221 , G06V20/54 , G06V10/82 , G06V10/774
Abstract: Methods, systems, and apparatus for predicting future trajectories of agents in an environment. In one aspect, a system comprises one or more computers configured to receive a data set comprising multiple training examples. The training examples include scene data comprising respective agent data for multiple agents and a ground truth trajectory for a target agent that represents ground truth motion of the target agent after a corresponding time point. The one or more computers obtain data identifying one or more of the multiple agents as non-causal agents for each training example. A non-causal agent is an agent whose states do not cause the ground truth trajectory for the target agent to change. The one or more computers generate a respective modified training example from each of the multiple training examples.
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公开(公告)号：US11693415B2
公开(公告)日：2023-07-04
申请号：US16676379
申请日：2019-11-06
Applicant: Waymo LLC
Inventor： Khaled Refaat , Chi Pang Lam
IPC: G01C22/00 , G05D1/00 , G05D1/02 , G06N3/04 , G01C21/34 , G06N3/08 , G06N3/006 , G06N20/20 , G08G1/16 , G06N3/045 , G06N5/01
CPC classification number: G05D1/0221 , G01C21/34 , G05D1/0088 , G06N3/006 , G06N3/04 , G06N3/045 , G06N3/08 , G06N5/01 , G06N20/20 , G08G1/167 , G05D2201/0213
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating cut-in probabilities of agents surrounding a vehicle. One of the methods includes obtaining agent trajectory data for one or more agents in an environment; obtaining vehicle trajectory data of a vehicle in the environment; and processing a network input generated from the agent trajectory data and vehicle trajectory data using a neural network to generate a cut-in output, wherein the cut-in output comprises respective cut-in probabilities for each of a plurality of locations in the environment, wherein the respective cut-in probability for each location that is a current location of one of the one or more agents characterizes a likelihood that the agent in the current location will intersect with a planned future location of the vehicle within a predetermined amount of time.
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公开(公告)号：US11657268B1
公开(公告)日：2023-05-23
申请号：US16586257
申请日：2019-09-27
Applicant: Waymo LLC
Inventor： Khaled Refaat , Kai Ding
CPC classification number: G06N3/08 , G06N3/04 , G05D1/0088 , G05D1/0221 , G05D2201/0213
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a neural network configured to receive a network input and to assign a respective score to each of a plurality of locations in the network input. In one aspect, a method includes obtaining a training input and a corresponding ground truth output; processing the training input to generate a training output; computing a loss for the training input, comprising: selecting a plurality of candidate locations; setting to zero the training scores for any location in the selected candidate locations that has a ground truth score below a threshold value; for each of a plurality of pairs of locations in the selected candidate locations: computing a pair-wise loss for the pair; and combining the pair-wise losses to compute the loss for the training input; and determining an update to the current values of the parameters.
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14.发明申请公开(公告)号：US20230015880A1
公开(公告)日：2023-01-19
申请号：US17375620
申请日：2021-07-14
Applicant: WAYMO LLC
Inventor： Khaled Refaat
Abstract: Aspects of the disclosure provide for generating distributions for hypothetical or potentially occluded objects. For instance, a location for which to generate one or more distributions may be identified. Observations of road users by perception systems of a plurality of autonomous vehicles may be accessed. Each of these observations may identify a characteristic of one of the road users. A distribution of the characteristic for the location may be determined based on the observations. The distribution may be provided to one or more autonomous vehicles in order to enable the one or more autonomous vehicles to use the distribution to generate a characteristic for a hypothetical occluded road user and to respond to the hypothetical occluded road user.
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公开(公告)号：US20220405618A1
公开(公告)日：2022-12-22
申请号：US17354232
申请日：2021-06-22
Applicant: Waymo LLC
Inventor： Khaled Refaat , Yun Jia Guan , Jeonhyung Kang
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating roadway crossing intent labels for training a machine learning model to perform roadway crossing intent predictions. One of the methods includes obtaining data identifying a training input, the training input including data characterizing an agent in an environment as of a given time, wherein the agent is located in a vicinity of a roadway in the environment at the given time. Future data characterizing (i) the agent, (ii) the environment or (iii) both over a future time period that is after the given time is obtained. From the future data, an intent label that indicates a likelihood that the agent intended to cross the roadway at the given time is determined. The training input is associated with the intent label in training data for training the machine learning model.
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Patent Agency Ranking
公开(公告)号：US20210319287A1
公开(公告)日：2021-10-14
申请号：US16847528
申请日：2020-04-13
Applicant: Waymo LLC
Inventor： Khaled Refaat , Kai Ding
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining occupancies of surrounding agents. One of the methods includes obtaining scene data characterizing an environment at a current time point; processing a first network input generated from the scene data using a first neural network to generate an intermediate output; obtaining an identification of a future time point that is after the current time point; and generating, from the intermediate output and the future time point, an occupancy output, wherein the occupancy output comprises respective occupancy probabilities for each of a plurality of locations in the environment, wherein the respective occupancy probability for each location characterizes a likelihood that one or more agents will occupy the location at the future time point.
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公开(公告)号：US20210133582A1
公开(公告)日：2021-05-06
申请号：US16671019
申请日：2019-10-31
Applicant: Waymo LLC
Inventor： Khaled Refaat , Kai Ding , Stephane Ross
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a neural network having a plurality of sub neural networks to assign respective confidence scores to one or more candidate future trajectories for an agent. Each confidence score indicates a predicted likelihood that the agent will move along the corresponding candidate future trajectory in the future. In one aspect, a method includes using the first sub neural network to generate a training intermediate representation; using the second sub neural network to generate respective training confidence scores; using a trajectory generation neural network to generate a training trajectory generation output; computing a first loss and a second loss; and determining an update to the current values of the parameters of the first and second sub neural networks.
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公开(公告)号：US20210132619A1
公开(公告)日：2021-05-06
申请号：US16676379
申请日：2019-11-06
Applicant: Waymo LLC
Inventor： Khaled Refaat , Chi Pang Lam
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating cut-in probabilities of agents surrounding a vehicle. One of the methods includes obtaining agent trajectory data for one or more agents in an environment; obtaining vehicle trajectory data of a vehicle in the environment; and processing a network input generated from the agent trajectory data and vehicle trajectory data using a neural network to generate a cut-in output, wherein the cut-in output comprises respective cut-in probabilities for each of a plurality of locations in the environment, wherein the respective cut-in probability for each location that is a current location of one of the one or more agents characterizes a likelihood that the agent in the current location will intersect with a planned future location of the vehicle within a predetermined amount of time.
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公开(公告)号：US20200333794A1
公开(公告)日：2020-10-22
申请号：US16922798
申请日：2020-07-07
Applicant: Waymo LLC
Inventor： Khaled Refaat , Stephane Ross
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a representation of a trajectory of a target agent in an environment. In one aspect, the representation of the trajectory of the target agent in the environment is a concatenation of a plurality of channels, where each channel is represented as a two-dimensional array of data values. Each position in each channel corresponds to a respective spatial position in the environment, and corresponding positions in different channels correspond to the same spatial position in the environment. The channels include a time channel and a respective motion channel corresponding to each motion parameter in a predetermined set of motion parameters.
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公开(公告)号：US20240278802A1
公开(公告)日：2024-08-22
申请号：US18393418
申请日：2023-12-21
Applicant: Waymo LLC
Inventor： Khaled Refaat , Haoyu Chen , Wei Chai , Alisha Saxena
IPC: B60W60/00 , G06F18/214 , G06F18/2415 , G06N3/08 , G06V20/56
CPC classification number: B60W60/001 , G06F18/214 , G06F18/2415 , G06N3/08 , G06V20/56
Abstract: Data representing a set of predicted trajectories and a planned trajectory for an autonomous vehicle is obtained. A predictability score for the planned trajectory can be determined based on a comparison of the planned trajectory to the set of predicted trajectories for the autonomous vehicle. The predictability score indicates a level of predictability of the planned trajectory. A determination can be made, based at least on the predictability score, whether to initiate travel with the autonomous vehicle along the planned trajectory. In response to determining to initiate travel with the autonomous vehicle along the planned trajectory, a control system can be directed to maneuver the autonomous vehicle along the planned trajectory.
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