-
公开(公告)号：US20240411311A1
公开(公告)日：2024-12-12
申请号：US18812632
申请日：2024-08-22
Applicant: Aurora Operations, Inc.
Inventor： Scott K. Boehmke
IPC: G05D1/00 , G01C21/34 , G01S7/481 , G01S7/497 , G01S17/931
Abstract: A planar-beam, light detection and ranging (PLADAR) system can include a laser scanner that emits a planar-beam, and a detector array that detects reflected light from the planar beam.
-
公开(公告)号：US20240369977A1
公开(公告)日：2024-11-07
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.
-
公开(公告)号：US20240367688A1
公开(公告)日：2024-11-07
申请号：US18658674
申请日：2024-05-08
Applicant: Aurora Operations, Inc.
Inventor： Alexander Yuhao Cui , Abbas Sadat , Sergio Casas , Renjie Liao , Raquel Urtasun
Abstract: Systems and methods are disclosed for motion forecasting and planning for autonomous vehicles. For example, a plurality of future traffic scenarios are determined by modeling a joint distribution of actor trajectories for a plurality of actors, as opposed to an approach that models actors individually. As another example, a diversity objective is evaluated that rewards sampling of the future traffic scenarios that require distinct reactions from the autonomous vehicle. An estimated probability for the plurality of future traffic scenarios can be determined and used to generate a contingency plan for motion of the autonomous vehicle. The contingency plan can include at least one initial short-term trajectory intended for immediate action of the AV and a plurality of subsequent long-term trajectories associated with the plurality of future traffic scenarios.
-
公开(公告)号：US20240361767A1
公开(公告)日：2024-10-31
申请号：US18655950
申请日：2024-05-06
Applicant: Aurora Operations, Inc.
Inventor： Albert Costa , Michael L. Phillips , Michael Bode
CPC classification number: G05D1/0223 , B60W30/0953 , G01C21/3415 , G05D1/227 , G05D1/644 , G05D1/65 , G05D1/692 , G05D1/81
Abstract: Systems and methods for controlling the motion of an autonomous are provided. In one example embodiment, a computer implemented method includes obtaining, by one or more computing devices on-board an autonomous vehicle, data associated with one or more objects that are proximate to the autonomous vehicle. The data includes a predicted path of each respective object. The method includes identifying at least one object as an object of interest based at least in part on the data associated with the object of interest. The method includes generating cost data associated with the object of interest. The method includes determining a motion plan for the autonomous vehicle based at least in part on the cost data associated with the object of interest. The method includes providing data indicative of the motion plan to one or more vehicle control systems to implement the motion plan for the autonomous vehicle.
-
公开(公告)号：US12130620B2
公开(公告)日：2024-10-29
申请号：US18175748
申请日：2023-02-28
Applicant: Aurora Operations, Inc.
Inventor： Scott C. Poeppel , Noah Zych , Matthew Shaw Wood , Dirk John Vandenberg, III
CPC classification number: G05D1/0022 , G05D1/0088 , G05D1/0221 , G07C5/008 , G08G1/0175
Abstract: Systems and methods are provided for remotely detecting a status associated with an autonomous vehicle and generating control actions in response to such detections. In one example, a computing system can access a third-party communication associated with an autonomous vehicle. The computing system can determine, based at least in part on the third-party communication, a predetermined identifier associated with the autonomous vehicle. The computing system can determine, based at least in part on the third-party communication, a status associated with the autonomous vehicle, and transmit one or more control messages to the autonomous vehicle based at least in part on the predetermined identifier and the status associated with the autonomous vehicle.
-
Patent Agency Ranking
公开(公告)号：US20240351610A1
公开(公告)日：2024-10-24
申请号：US18735825
申请日：2024-06-06
Applicant: Aurora Operations, Inc.
Inventor： James Robert Curry , Fangrong Peng
IPC: B60W60/00 , G01S17/89 , G01S17/931
CPC classification number: B60W60/001 , G01S17/89 , G01S17/931 , B60W2556/40
Abstract: Various examples are directed to systems and methods for directing a field-of-view of a first sensor positioned on an autonomous vehicle. In one example, at least one processor selects a goal location on at least one travel way in an environment of the autonomous vehicle. The selecting of the goal location is based at least in part on map data describing at least one travel way in an environment of the autonomous vehicle and pose data describing a position of the autonomous vehicle in the environment. The at least one processor determines a field-of-view position to direct the first sensor towards the goal location based at least in part on the sensor position data. The at least one processor sends a field-of-view command to the first sensor. The field-of-view command modifies the field-of-view of the first sensor based on the field-of-view position.
-
公开(公告)号：US12110042B1
公开(公告)日：2024-10-08
申请号：US17501480
申请日：2021-10-14
Applicant: Aurora Operations, Inc.
Inventor： Anh Tuan Hoang , Harshayu Girase , Sai Bhargav Yalamanchi , Micol Marchetti-Bowick
CPC classification number: B60W60/00274 , B60W40/04 , B60W50/0097 , B60W50/06 , B60W60/0011 , G06N3/044 , G06N3/045 , B60W2554/4041 , B60W2554/4044
Abstract: Example aspects of the present disclosure describe the generation of more realistic trajectories for a moving actor with a hybrid technique using an algorithmic trajectory shaper in a machine-learned trajectory prediction pipeline. In this manner, for example, systems and methods of the present disclosure leverage the predictive power of machine-learning approaches combined with a priori knowledge about physically realistic trajectories for a given actor as encoded in an algorithmic approach.
-
公开(公告)号：US20240310835A1
公开(公告)日：2024-09-19
申请号：US18674442
申请日：2024-05-24
Applicant: Aurora Operations, Inc.
Inventor： David McAllister Bradley , Chenggang Liu , Daoyuan Jia
CPC classification number: G05D1/0212 , B60W60/0011
Abstract: Systems and methods for vehicle spatial path sampling are provided. The method includes obtaining an initial travel path for an autonomous vehicle from a first location to a second location and vehicle configuration data indicative of one or more physical constraints of the autonomous vehicle. The method includes determining one or more secondary travel paths for the autonomous vehicle from the first location to the second location based on the initial travel path and the vehicle configuration data. The method includes generating a spatial envelope based on the one or more secondary travel paths that indicates a plurality of lateral offsets from the initial travel path. And, the method includes generating a plurality of trajectories for the autonomous vehicle to travel from the first location to the second location such that each of the plurality of trajectories include one or more lateral offsets identified by the spatial envelope.
-
69.发明授权公开(公告)号：US12091043B2
公开(公告)日：2024-09-17
申请号：US18109113
申请日：2023-02-13
Applicant: Aurora Operations, Inc.
Inventor： Jake Charland , Ethan Eade , Karthik Lakshmanan , Daniel Munoz , Samuel Sean , Yuchen Xie , Luona Yang
CPC classification number: B60W60/001 , G06T15/08 , B60W2420/403 , B60W2420/408 , B60W2510/0638
Abstract: A method may include obtaining lidar data comprising a plurality of lidar returns from an environment of an autonomous vehicle. The lidar data may be processed with a machine learning model to generate, for the plurality of lidar returns, a plurality of first outputs that each identify a respective lidar return as belonging to an object or non-object and a plurality of second outputs that identify lidar returns belonging to objects as harmful or non-harmful to the autonomous vehicle. A subset of the lidar returns identified as belonging to objects that (i) do not correspond to any of a plurality of pre-classified objects and (ii) were identified as harmful to the autonomous vehicle may be determined. The autonomous vehicle may be controlled based at least in part on the subset of lidar returns.
-
公开(公告)号：US20240300525A1
公开(公告)日：2024-09-12
申请号：US18269209
申请日：2021-12-17
Applicant: Aurora Operations, Inc.
Inventor： James Andrew Bagnell , Arun Venkatraman , Sanjiban Choudhury , Venkatraman Narayanan
CPC classification number: B60W60/001 , G06N20/00 , B60W2554/4026 , B60W2554/4029 , B60W2556/10
Abstract: Systems and methods related to controlling an autonomous vehicle (“AV”) are described herein. Implementations can process actor(s) from a past episode of locomotion of a vehicle, and stream(s) in an environment of the vehicle during the past episode to generate predicted output(s). The actor(s) may each be associated with a corresponding object in the environment of the vehicle, and the stream(s) may each represent candidate navigation paths in the environment of the vehicle. Further, implementations can process the predicted output(s) to generate further predicted output(s), and can compare the predicted output(s) to associated reference label(s). The processing can be performed utilizing layer(s) or distinct, additional layer(s) of machine learning (“ML”) model(s). Implementations can update the layer(s) or the additional layer(s) based on the comparing, and subsequently use the ML model(s) in controlling the AV.
-
-
-
-
-
-
-
-
-
Search Results
265
records
(took 0.029 seconds)
