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公开(公告)号：US12210947B2
公开(公告)日：2025-01-28
申请号：US18238741
申请日：2023-08-28
Applicant: Waymo LLC
Inventor： Xin Zhou , Roshni Cooper , Michael James
Abstract: The technology relates to using on-board sensor data, off-board information and a deep learning model to classify road wetness and/or to perform a regression analysis on road wetness based on a set of input information. Such information includes on-board and/or off-board signals obtained from one or more sources including on-board perception sensors, other on-board modules, external weather measurement, external weather services, etc. The ground truth includes measurements of water film thickness and/or ice coverage on road surfaces. The ground truth, on-board and off-board signals are used to build the model. The constructed model can be deployed in autonomous vehicles for classifying/regressing the road wetness with on-board and/or off-board signals as the input, without referring to the ground truth. The model can be applied in a variety of ways to enhance autonomous vehicle operation, for instance by altering current driving actions, modifying planned routes or trajectories, activating on-board cleaning systems, etc.
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公开(公告)号：US11221399B2
公开(公告)日：2022-01-11
申请号：US16217899
申请日：2018-12-12
Applicant: Waymo LLC
Inventor： Clayton Kunz , Christian Lauterbach , Roshni Cooper
Abstract: Aspects of the disclosure relate to detecting spurious objects. For instance, a model may be trained using raining data including a plurality of LIDAR data points generated by a LIDAR sensor of a vehicle. Each given LIDAR data point includes location information and intensity information, and is associated with waveform data for that given LIDAR data point. At least one of the plurality of LIDAR data points is further associated with a label identifying spurious objects through which the vehicle is able to drive. The model and/or a plurality of heuristics may then be provided to a vehicle in order to allow the vehicle to determine LIDAR data points that correspond to spurious objects. These LIDAR data points may then be filtered from sensor data, and the filtered sensor data may be used to control the vehicle in an autonomous driving mode.
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公开(公告)号：US20230409971A1
公开(公告)日：2023-12-21
申请号：US18238741
申请日：2023-08-28
Applicant: Waymo LLC
Inventor： Xin Zhou , Roshni Cooper , Michael James
CPC classification number: G06N20/00 , B60W40/06 , B60W60/0015 , G05D1/0231 , G05D1/0255 , G05D1/0257 , G06N3/08 , G05D2201/0213 , B60W2555/20 , B60W2420/52 , B60W2420/42 , B60W2420/54
Abstract: The technology relates to using on-board sensor data, off-board information and a deep learning model to classify road wetness and/or to perform a regression analysis on road wetness based on a set of input information. Such information includes on-board and/or off-board signals obtained from one or more sources including on-board perception sensors, other on-board modules, external weather measurement, external weather services, etc. The ground truth includes measurements of water film thickness and/or ice coverage on road surfaces. The ground truth, on-board and off-board signals are used to build the model. The constructed model can be deployed in autonomous vehicles for classifying/regressing the road wetness with on-board and/or off-board signals as the input, without referring to the ground truth. The model can be applied in a variety of ways to enhance autonomous vehicle operation, for instance by altering current driving actions, modifying planned routes or trajectories, activating on-board cleaning systems, etc.
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公开(公告)号：US20210354723A1
公开(公告)日：2021-11-18
申请号：US16872502
申请日：2020-05-12
Applicant: Waymo LLC
Inventor： Courtney McCool , Roshni Cooper , Timothy Yang , Yuchi Wang
Abstract: Aspects of the disclosure provide methods for controlling a first vehicle having an autonomous driving mode. In one instance, sensor data generated by one or more sensors of the first vehicle may be received. A splash and characteristics of the splash may be detected from the sensor data using a classifier. A severity of a puddle may be determined based on the characteristics of the splash and a speed of a second vehicle that caused the splash. The first vehicle may be controlled based on the severity. In another instance, a location of a puddle relative to a tire of a second vehicle is estimated using sensor data generated by one or more sensors of the first vehicle. A severity of the puddle may be determined based on the estimated location. The first vehicle may be controlled based on the severity.
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公开(公告)号：US20220292402A1
公开(公告)日：2022-09-15
申请号：US17828196
申请日：2022-05-31
Applicant: Waymo LLC
Inventor： Xin Zhou , Roshni Cooper , Michael James
Abstract: The technology relates to using on-board sensor data, off-board information and a deep learning model to classify road wetness and/or to perform a regression analysis on road wetness based on a set of input information. Such information includes on-board and/or off-board signals obtained from one or more sources including on-board perception sensors, other on-board modules, external weather measurement, external weather services, etc. The ground truth includes measurements of water film thickness and/or ice coverage on road surfaces. The ground truth, on-board and off-board signals are used to build the model. The constructed model can be deployed in autonomous vehicles for classifying/regressing the road wetness with on-board and/or off-board signals as the input, without referring to the ground truth. The model can be applied in a variety of ways to enhance autonomous vehicle operation, for instance by altering current driving actions, modifying planned routes or trajectories, activating on-board cleaning systems, etc.
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Patent Agency Ranking
公开(公告)号：US20220155415A1
公开(公告)日：2022-05-19
申请号：US17544242
申请日：2021-12-07
Applicant: Waymo LLC
Inventor： Clayton Kunz , Christian Lauterbach , Roshni Cooper
Abstract: Aspects of the disclosure relate to detecting spurious objects. For instance, a model may be trained using raining data including a plurality of LIDAR data points generated by a LIDAR sensor of a vehicle. Each given LIDAR data point includes location information and intensity information, and is associated with waveform data for that given LIDAR data point. At least one of the plurality of LIDAR data points is further associated with a label identifying spurious objects through which the vehicle is able to drive. The model and/or a plurality of heuristics may then be provided to a vehicle in order to allow the vehicle to determine LIDAR data points that correspond to spurious objects. These LIDAR data points may then be filtered from sensor data, and the filtered sensor data may be used to control the vehicle in an autonomous driving mode.
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公开(公告)号：US20210383269A1
公开(公告)日：2021-12-09
申请号：US16893664
申请日：2020-06-05
Applicant: Waymo LLC
Inventor： Xin Zhou , Roshni Cooper , Michael James
Abstract: The technology relates to using on-board sensor data, off-board information and a deep learning model to classify road wetness and/or to perform a regression analysis on road wetness based on a set of input information. Such information includes on-board and/or off-board signals obtained from one or more sources including on-board perception sensors, other on-board modules, external weather measurement, external weather services, etc. The ground truth includes measurements of water film thickness and/or ice coverage on road surfaces. The ground truth, on-board and off-board signals are used to build the model. The constructed model can be deployed in autonomous vehicles for classifying/regressing the road wetness with on-board and/or off-board signals as the input, without referring to the ground truth. The model can be applied in a variety of ways to enhance autonomous vehicle operation, for instance by altering current driving actions, modifying planned routes or trajectories, activating on-board cleaning systems, etc.
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公开(公告)号：US11775870B2
公开(公告)日：2023-10-03
申请号：US17978287
申请日：2022-11-01
Applicant: WAYMO LLC
Inventor： Xin Zhou , Roshni Cooper , Michael James
CPC classification number: G06N20/00 , B60W40/06 , B60W60/0015 , G05D1/0231 , G05D1/0255 , G05D1/0257 , G06N3/08 , B60W2420/42 , B60W2420/52 , B60W2420/54 , B60W2555/20 , G05D2201/0213
Abstract: The technology relates to using on-board sensor data, off-board information and a deep learning model to classify road wetness and/or to perform a regression analysis on road wetness based on a set of input information. Such information includes on-board and/or off-board signals obtained from one or more sources including on-board perception sensors, other on-board modules, external weather measurement, external weather services, etc. The ground truth includes measurements of water film thickness and/or ice coverage on road surfaces. The ground truth, on-board and off-board signals are used to build the model. The constructed model can be deployed in autonomous vehicles for classifying/regressing the road wetness with on-board and/or off-board signals as the input, without referring to the ground truth. The model can be applied in a variety of ways to enhance autonomous vehicle operation, for instance by altering current driving actions, modifying planned routes or trajectories, activating on-board cleaning systems, etc.
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公开(公告)号：US20230055334A1
公开(公告)日：2023-02-23
申请号：US17978287
申请日：2022-11-01
Applicant: WAYMO LLC
Inventor： Xin Zhou , Roshni Cooper , Michael James
Abstract: The technology relates to using on-board sensor data, off-board information and a deep learning model to classify road wemess and/or to perform a regression analysis on road wetness based on a set of input information. Such information includes on-board and/or off-board signals obtained from one or more sources including on-board perception sensors, other on-board modules. external weather measurement, external weather services, etc. The ground truth includes measurements of water film thickness and/or ice coverage on road surfaces. The ground truth, on-board and off-board signals are used to build the model. The constructed model can be deployed in autonomous vehicles for classifying/regressing the road wetness with on-board and/or off-board signals as the input, without referring to the ground truth. The model can be applied in a variety of ways to enhance autonomous vehicle operation, for instance by altering current driving actions, modifying planned routes or trajectories, activating on-board cleaning systems, etc.
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公开(公告)号：US11521130B2
公开(公告)日：2022-12-06
申请号：US17828196
申请日：2022-05-31
Applicant: Waymo LLC
Inventor： Xin Zhou , Roshni Cooper , Michael James
Abstract: The technology relates to using on-board sensor data, off-board information and a deep learning model to classify road wetness and/or to perform a regression analysis on road wetness based on a set of input information. Such information includes on-board and/or off-board signals obtained from one or more sources including on-board perception sensors, other on-board modules, external weather measurement, external weather services, etc. The ground truth includes measurements of water film thickness and/or ice coverage on road surfaces. The ground truth, on-board and off-board signals are used to build the model. The constructed model can be deployed in autonomous vehicles for classifying/regressing the road wetness with on-board and/or off-board signals as the input, without referring to the ground truth. The model can be applied in a variety of ways to enhance autonomous vehicle operation, for instance by altering current driving actions, modifying planned routes or trajectories, activating on-board cleaning systems, etc.
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