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公开(公告)号：US20240135727A1
公开(公告)日：2024-04-25
申请号：US18403316
申请日：2024-01-03
Applicant: WAYMO LLC
Inventor： Romain Thibaux , David Harrison Silver , Congrui Hetang
IPC: G06V20/56 , B60W30/18 , G06F18/214 , G06F18/2413 , G06V10/25
CPC classification number: G06V20/588 , B60W30/181 , B60W30/18154 , G06F18/214 , G06F18/24147 , G06V10/25 , B60W2420/403
Abstract: The technology relates to approaches for determining appropriate stopping locations at intersections for vehicles operating in a self-driving mode. While many intersections have stop lines painted on the roadway, many others have no such lines. Even if a stop line is present, the physical location may not match what is in store map data, which may be out of date due to construction or line repainting. Aspects of the technology employ a neural network that utilizes input training data and detected sensor data to perform classification, localization and uncertain estimation processes. Based on these processes, the system is able to evaluate distribution information for possible stop locations. The vehicle uses such information to determine an optimal stop point, which may or may not correspond to a stop line in the map data. This information is also used to update the existing map data, which can be shared with other vehicles.
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公开(公告)号：US11900697B2
公开(公告)日：2024-02-13
申请号：US17958711
申请日：2022-10-03
Applicant: WAYMO LLC
Inventor： Romain Thibaux , David Harrison Silver , Congrui Hetang
IPC: G06V20/56 , B60W30/18 , G06K9/62 , G06V10/25 , G06F18/214 , G06F18/2413
CPC classification number: G06V20/588 , B60W30/181 , B60W30/18154 , G06F18/214 , G06F18/24147 , G06V10/25 , B60W2420/42
Abstract: The technology relates to approaches for determining appropriate stopping locations at intersections for vehicles operating in a self-driving mode. While many intersections have stop lines painted on the roadway, many others have no such lines. Even if a stop line is present, the physical location may not match what is in store map data, which may be out of date due to construction or line repainting. Aspects of the technology employ a neural network that utilizes input training data and detected sensor data to perform classification, localization and uncertain estimation processes. Based on these processes, the system is able to evaluate distribution information for possible stop locations. The vehicle uses such information to determine an optimal stop point, which may or may not correspond to a stop line in the map data. This information is also used to update the existing map data, which can be shared with other vehicles.
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公开(公告)号：US20230023913A1
公开(公告)日：2023-01-26
申请号：US17958711
申请日：2022-10-03
Applicant: WAYMO LLC
Inventor： Romain Thibaux , David Harrison Silver , Congrui Hetang
Abstract: The technology relates to approaches for determining appropriate stopping locations at intersections for vehicles operating in a self-driving mode. While many intersections have stop lines painted on the roadway, many others have no such lines. Even if a stop line is present, the physical location may not match what is in store map data, which may be out of date due to construction or line repainting. Aspects of the technology employ a neural network that utilizes input training data and detected sensor data to perform classification, localization and uncertain estimation processes. Based on these processes, the system is able to evaluate distribution information for possible stop locations. The vehicle uses such information to determine an optimal stop point, which may or may not correspond to a stop line in the map data. This information is also used to update the existing map data, which can be shared with other vehicles.
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Patent Agency Ranking
公开(公告)号：US20230356748A1
公开(公告)日：2023-11-09
申请号：US17740215
申请日：2022-05-09
Applicant: Waymo LLC
Inventor： Congrui Hetang , Ningshan Zhang
CPC classification number: B60W60/0011 , G05B13/0265
Abstract: A system identifies a set of input data including a roadgraph identifying an intermediate autonomous vehicle (AV) driving path related to a scene representing an environment proximate an AV. The intermediate AV driving path reflects a modification to an initial (AV) driving path to avoid one or more obstacles obstructing the initial AV driving path. The system performs, using the set of input data, a path adjustment operation that identifies one or more candidate AV driving paths based on the intermediate AV driving path and determines a cost value for each of the candidate AV driving paths. The system identifies, among the candidate AV driving paths, a final AV driving path having a cost value that satisfies an evaluation criterion. The final AV driving path is to be included in the set of training data as a target output paired with training input including scene data identifying the scene.
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公开(公告)号：US11749000B2
公开(公告)日：2023-09-05
申请号：US17131232
申请日：2020-12-22
Applicant: WAYMO LLC
Inventor： Romain Thibaux , David Harrison Silver , Congrui Hetang
IPC: G06K9/00 , B60W30/18 , G06K9/32 , G06K9/62 , G06V20/56 , G06V10/25 , G06F18/214 , G06F18/2413
CPC classification number: G06V20/588 , B60W30/181 , B60W30/18154 , G06F18/214 , G06F18/24147 , G06V10/25 , B60W2420/42
Abstract: The technology relates to approaches for determining appropriate stopping locations at intersections for vehicles operating in a self-driving mode. While many intersections have stop lines painted on the roadway, many others have no such lines. Even if a stop line is present, the physical location may not match what is in store map data, which may be out of date due to construction or line repainting. Aspects of the technology employ a neural network that utilizes input training data and detected sensor data to perform classification, localization and uncertain estimation processes. Based on these processes, the system is able to evaluate distribution information for possible stop locations. The vehicle uses such information to determine an optimal stop point, which may or may not correspond to a stop line in the map data. This information is also used to update the existing map data, which can be shared with other vehicles.
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公开(公告)号：US20220402521A1
公开(公告)日：2022-12-22
申请号：US17349450
申请日：2021-06-16
Applicant: Waymo LLC
Inventor： Congrui Hetang
Abstract: A system includes a memory device, and a processing device, operatively coupled to the memory device, to receive a set of input data including a roadgraph. The roadgraph includes an autonomous vehicle driving path. The processing device is further to determine that the autonomous vehicle driving path is affected by one or more obstacles, identify a set of candidate paths that avoid the one or more obstacles, each candidate path of the set of candidate paths being associated with a cost value, select, from the set of candidate paths, a candidate path with an optimal cost value to obtain a selected candidate path, generate a synthetic scene based on the selected candidate path, and train a machine learning model to navigate an autonomous vehicle based on the synthetic scene.
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公开(公告)号：US20220402520A1
公开(公告)日：2022-12-22
申请号：US17349489
申请日：2021-06-16
Applicant: Waymo LLC
Inventor： Congrui Hetang , Yi Shen , Youjie Zhou , Jiyang Gao
Abstract: A system includes a memory device, and a processing device, operatively coupled to the memory device, to receive a set of input data including a roadgraph, the roadgraph including an autonomous vehicle driving path, modify the roadgraph to obtain a modified roadgraph by adjusting a trajectory of the autonomous vehicle driving path, place a set of artifacts along one or more lane boundaries of the modified roadgraph to generate a synthetic scene, and train a machine learning model used to navigate an autonomous vehicle based on the synthetic scene.
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公开(公告)号：US20220198199A1
公开(公告)日：2022-06-23
申请号：US17131232
申请日：2020-12-22
Applicant: WAYMO LLC
Inventor： Romain Thibaux , David Harrison Silver , Congrui Hetang
Abstract: The technology relates to approaches for determining appropriate stopping locations at intersections for vehicles operating in a self-driving mode. While many intersections have stop lines painted on the roadway, many others have no such lines. Even if a stop line is present, the physical location may not match what is in store map data, which may be out of date due to construction or line repainting. Aspects of the technology employ a neural network that utilizes input training data and detected sensor data to perform classification, localization and uncertain estimation processes. Based on these processes, the system is able to evaluate distribution information for possible stop locations. The vehicle uses such information to determine an optimal stop point, which may or may not correspond to a stop line in the map data. This information is also used to update the existing map data, which can be shared with other vehicles.
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