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公开(公告)号：US20230400859A1
公开(公告)日：2023-12-14
申请号：US18203230
申请日：2023-05-30
Applicant: Waymo LLC
Inventor： Vishu Goyal , Kai Ding
CPC classification number: G05D1/0214 , G05D1/0088 , G05D2201/0213
Abstract: Jaywalking behaviors of vulnerable road users (VRUs) such as cyclists or pedestrians can be predicted. Location data is obtained that identifies a location of a VRU within a vicinity of a vehicle. Environmental data is obtained that describes an environment of the VRU, where the environmental data identifies a set of environmental features in the environment of the VRU. The system can determine a nominal heading of the VRU, and generate a set of predictive inputs that indicate, for each of at least a subset of the set of environmental features, a physical relationship between the VRU and the environmental feature. The physical relationship can be determined with respect to the nominal heading of the VRU and the location of the VRU. The set of predictive inputs can be processed with a heading estimation model to generate a predicted heading offset (e.g., a target heading offset) for the VRU.
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公开(公告)号：US11841704B2
公开(公告)日：2023-12-12
申请号：US17089046
申请日：2020-11-04
Applicant: Waymo LLC
Inventor： Vishu Goyal , Stéphane Ross , Kai Ding
CPC classification number: G05D1/0088 , G05D1/0214 , G05D2201/0213
Abstract: To operate an autonomous vehicle, a rail agent is detected in a vicinity of the autonomous vehicle using a detection system. One or more tracks are determined on which the detected rail agent is possibly traveling, and possible paths for the rail agent are predicted based on the determined one or more tracks. One or more motion paths are determined for one or more probable paths from the possible paths, and a likelihood for each of the one or more probable paths is determined based on each motion plan. A path for the autonomous vehicle is then determined based on a most probable path associated with a highest likelihood for the rail agent, and the autonomous vehicle is operated using the determined path.
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公开(公告)号：US11815892B2
公开(公告)日：2023-11-14
申请号：US17333823
申请日：2021-05-28
Applicant: Waymo LLC
Inventor： Kai Ding , Khaled Refaat , Stephane Ross
CPC classification number: G05D1/0088 , G05D1/0214 , G05D1/0221 , G06N5/022 , G05D2201/0213
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying high-priority agents in the vicinity of a vehicle and, for only those agents which are high priority agents, generating data characterizing the agents using a first prediction model. In a first aspect, a system identifies multiple agents in an environment in a vicinity of a vehicle. The system generates a respective importance score for each of the agents by processing a feature representation of each agent using an importance scoring model. The importance score for an agent characterizes an estimated impact of the agent on planning decisions generated by a planning system of the vehicle which plans a future trajectory of the vehicle. The system identifies, as high-priority agents, a proper subset of the plurality of agents with the highest importance scores.
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公开(公告)号：US11783614B2
公开(公告)日：2023-10-10
申请号：US17330511
申请日：2021-05-26
Applicant: Waymo LLC
Inventor： Jared Stephen Russell , Kai Ding
CPC classification number: G06V40/10 , B60W30/0956 , G05D1/0088 , G05D1/0214 , G05D1/0276 , G06V20/58 , G06V40/103 , G08G1/166 , G05D2201/0213 , G06T2207/30196 , G06T2207/30261
Abstract: The technology relates to controlling a vehicle in an autonomous driving mode. For instance, sensor data identifying an object in an environment of the vehicle may be received. A grid including a plurality of cells may be projected around the object. For each given one of the plurality of cells, a likelihood that the object will enter the given one within a period of time into the future is predicted. A contour is generated based on the predicted likelihoods. The vehicle is then controlled in the autonomous driving mode in order to avoid an area within the contour.
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公开(公告)号：US11698639B2
公开(公告)日：2023-07-11
申请号：US16892579
申请日：2020-06-04
Applicant: Waymo LLC
Inventor： Vishu Goyal , Kai Ding
CPC classification number: G05D1/0214 , G05D1/0088 , G05D2201/0213
Abstract: Jaywalking behaviors of vulnerable road users (VRUs) such as cyclists or pedestrians can be predicted. Location data is obtained that identifies a location of a VRU within a vicinity of a vehicle. Environmental data is obtained that describes an environment of the VRU, where the environmental data identifies a set of environmental features in the environment of the VRU. The system can determine a nominal heading of the VRU, and generate a set of predictive inputs that indicate, for each of at least a subset of the set of environmental features, a physical relationship between the VRU and the environmental feature. The physical relationship can be determined with respect to the nominal heading of the VRU and the location of the VRU. The set of predictive inputs can be processed with a heading estimation model to generate a predicted heading offset (e.g., a target heading offset) for the VRU.
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Patent Agency Ranking
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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公开(公告)号：US20220371624A1
公开(公告)日：2022-11-24
申请号：US17325952
申请日：2021-05-20
Applicant: Waymo LLC
Inventor： Vishu Goyal , Shubham Gupta , Kai Ding
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium that generates path prediction data for agents in the vicinity of an autonomous vehicle using one or more machine learning models. One of the methods includes identifying an agent in a vicinity of an autonomous vehicle navigating through an environment and determining that the agent is within a vicinity of a crossing zone across a roadway. The crossing zone can be a marked crossing zone or an unmarked crossing zone. For example, the crossing zone can be an unmarked crossing zone that has been identified based on previous observations of agents crossing the roadway. In response to determining that the agent is within a vicinity of a crossing zone: (i) features of the agent and of the crossing zone can be obtained; (ii) a first input that includes the features can be processed using a first machine learning model that is configured to generate a first crossing prediction that characterizes future crossing behavior of the agent, and (iii) a predicted path for the agent for crossing the roadway can be determined from at least the first crossing prediction.
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公开(公告)号：US20220137623A1
公开(公告)日：2022-05-05
申请号：US17089046
申请日：2020-11-04
Applicant: Waymo LLC
Inventor： Vishu Goyal , Stéphane Ross , Kai Ding
Abstract: To operate an autonomous vehicle, a rail agent is detected in a vicinity of the autonomous vehicle using a detection system. One or more tracks are determined on which the detected rail agent is possibly traveling, and possible paths for the rail agent are predicted based on the determined one or more tracks. One or more motion paths are determined for one or more probable paths from the possible paths, and a likelihood for each of the one or more probable paths is determined based on each motion plan. A path for the autonomous vehicle is then determined based on a most probable path associated with a highest likelihood for the rail agent, and the autonomous vehicle is operated using the determined path.
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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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