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公开(公告)号：US20220245312A1
公开(公告)日：2022-08-04
申请号：US17725045
申请日：2022-04-20
Applicant: Aurora Operations, Inc.
Inventor： John Michael Wyrwas , Jessica Elizabeth Smith , Simon Box
Abstract: Logged data from an autonomous vehicle is processed to generate augmented data. The augmented data describes an actor in an environment of the autonomous vehicle, the actor having an associated actor type and an actor motion behavior characteristic. The augmented data may be varied to create different sets of augmented data. The sets of augmented data can be used to create one or more simulation scenarios that in turn are used to produce machine learning models to control the operation of autonomous vehicles.
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公开(公告)号：US20220230026A1
公开(公告)日：2022-07-21
申请号：US17713782
申请日：2022-04-05
Applicant: Aurora Operations, Inc.
Inventor： Jean-Sebastien Valois , Thomas Pilarski , Daniel Munoz
Abstract: In techniques disclosed herein, machine learning models can be utilized in the control of autonomous vehicle(s), where the machine learning models are trained using automatically generated training instances. In some such implementations, a label corresponding to an object in a labeled instance of training data can be mapped to the corresponding instance of unlabeled training data. For example, an instance of sensor data can be captured using one or more sensors of a first sensor suite of a first vehicle can be labeled. The label(s) can be mapped to an instance of data captured using one or more sensors of a second sensor suite of a second vehicle.
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公开(公告)号：US20220012388A1
公开(公告)日：2022-01-13
申请号：US17483506
申请日：2021-09-23
Applicant: Aurora Operations, Inc.
Inventor： John Michael Wyrwas , Jessica Elizabeth Smith , Simon Box
Abstract: Logged data from an autonomous vehicle is processed to generate augmented data. The augmented data describes an actor in an environment of the autonomous vehicle, the actor having an associated actor type and an actor motion behavior characteristic. The augmented data may be varied to create different sets of augmented data. The sets of augmented data can be used to create one or more simulation scenarios that in turn are used to produce machine learning models to control the operation of autonomous vehicles.
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公开(公告)号：US20250165869A1
公开(公告)日：2025-05-22
申请号：US19028221
申请日：2025-01-17
Applicant: Aurora Operations, Inc.
Inventor： Davis Edward KING , Yan LI
Abstract: A method includes obtaining a first track associated with an object. A first set of parameters is generated based on the first track. Measurement data are obtained from one or more sensors. A first set of features are extracted from the measurement data. Based on the first set of parameters and the first set of features, a second set of parameters are generated by a machine learning model. The second set of parameters represent an adjustment to the first set of parameters. Based on the second set of parameters, the first track is adjusted to generate a second track associated with the object. The second track is provided to an autonomous vehicle control system for autonomous control of a vehicle.
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公开(公告)号：US20250138170A1
公开(公告)日：2025-05-01
申请号：US18883809
申请日：2024-09-12
Applicant: Aurora Operations, Inc.
Inventor： Andrew Steil Michaels , Tsz Chun Wong
IPC: G01S7/497 , G01S7/481 , G01S17/931
Abstract: A device testing circuit for a LIDAR sensor system of a vehicle includes a first splitter optically coupled to a first connection and to a first device, the first device configured to generate a first output signal in response to receiving a first optical signal from the first connection through the first splitter, and a second splitter optically coupled to a second connection and to a second device, the second device configured to generate a second output signal in response to receiving a second optical signal from the second connection through the second splitter. The first splitter and the second splitter are optically coupled to each other.
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Patent Agency Ranking
公开(公告)号：US20250130909A1
公开(公告)日：2025-04-24
申请号：US19007149
申请日：2024-12-31
Applicant: Aurora Operations, Inc.
Inventor： Sivabalan Manivasagam , Shenlong Wang , Wei-Chiu Ma , Kelvin Ka Wing Wong , Wenyuan Zeng , Raquel Urtasun
Abstract: The present disclosure provides systems and methods that combine physics-based systems with machine learning to generate synthetic LiDAR data that accurately mimics a real-world LiDAR sensor system. In particular, aspects of the present disclosure combine physics-based rendering with machine-learned models such as deep neural networks to simulate both the geometry and intensity of the LiDAR sensor. As one example, a physics-based ray casting approach can be used on a three-dimensional map of an environment to generate an initial three-dimensional point cloud that mimics LiDAR data. According to an aspect of the present disclosure, a machine-learned model can predict one or more dropout probabilities for one or more of the points in the initial three-dimensional point cloud, thereby generating an adjusted three-dimensional point cloud which more realistically simulates real-world LiDAR data.
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公开(公告)号：US20250117709A1
公开(公告)日：2025-04-10
申请号：US18982461
申请日：2024-12-16
Applicant: Aurora Operations, Inc.
Inventor： Sergio Casas , Cole Christian Gulino , Shun Da Suo , Raquel Urtasun
Abstract: The present disclosure provides systems and methods for training probabilistic object motion prediction models using non-differentiable representations of prior knowledge. As one example, object motion prediction models can be used by autonomous vehicles to probabilistically predict the future location(s) of observed objects (e.g., other vehicles, bicyclists, pedestrians, etc.). For example, such models can output a probability distribution that provides a distribution of probabilities for the future location(s) of each object at one or more future times. Aspects of the present disclosure enable these models to be trained using non-differentiable prior knowledge about motion of objects within the autonomous vehicle's environment such as, for example, prior knowledge about lane or road geometry or topology and/or traffic information such as current traffic control states (e.g., traffic light status).
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公开(公告)号：US20250116773A1
公开(公告)日：2025-04-10
申请号：US18481695
申请日：2023-10-05
Applicant: Aurora Operations, Inc.
Inventor： Emil Kadlec , Mark Lund , Justin Torgerson
IPC: G01S7/4911 , B60W10/18 , B60W10/20 , B60W60/00 , G01S7/481 , G01S17/931
Abstract: A light detection and ranging sensor system includes a laser source configured to output a source beam; a modulator configured to receive a modulation signal and modulate the source beam based on the modulation signal to produce a modulated beam; an amplifier configured to amplify the modulated beam; and a protection circuit configured to detect, by evaluating at least one of the modulation signal or a parameter of the modulated beam, a condition associated with the modulated beam; and control input of the modulated beam to the amplifier in response to detecting the condition.
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公开(公告)号：US12259694B2
公开(公告)日：2025-03-25
申请号：US18656210
申请日：2024-05-06
Applicant: Aurora Operations, Inc.
Inventor： Abhishek Mohta , Fang-Chieh Chou , Carlos Vallespi-Gonzalez , Brian C. Becker , Nemanja Djuric
Abstract: Systems and methods are disclosed for detecting and predicting the motion of objects within the surrounding environment of a system such as an autonomous vehicle. For example, an autonomous vehicle can obtain sensor data from a plurality of sensors comprising at least two different sensor modalities (e.g., RADAR, LIDAR, camera) and fused together to create a fused sensor sample. The fused sensor sample can then be provided as input to a machine learning model (e.g., a machine learning model for object detection and/or motion prediction). The machine learning model can have been trained by independently applying sensor dropout to the at least two different sensor modalities. Outputs received from the machine learning model in response to receipt of the fused sensor samples are characterized by improved generalization performance over multiple sensor modalities, thus yielding improved performance in detecting objects and predicting their future locations, as well as improved navigation performance.
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60.发明申请公开(公告)号：US20250083519A1
公开(公告)日：2025-03-13
申请号：US18932239
申请日：2024-10-30
Applicant: Aurora Operations, Inc.
Inventor： Molly Castle Nix , Sean Chin , Dennis Zhao , Eric James Hanson
Abstract: The present disclosure provides an autonomous vehicle and associated interface system that includes multiple vehicle interface computing devices that provide redundant vehicle commands. As one example, an autonomous vehicle interface system can include a first vehicle interface computing device located within the autonomous vehicle and physically coupled to the autonomous vehicle. The first vehicle interface computing device can provide a first plurality of selectable vehicle commands to a human passenger of the autonomous vehicle. The autonomous vehicle interface system can further include a second vehicle interface computing device that provides a second plurality of selectable vehicle commands to the human passenger. For example, the second vehicle interface computing device can be the passenger's own device (e.g., smartphone). The second plurality of selectable vehicle commands can include at least some of the same vehicle commands as the first plurality of selectable vehicle commands.
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