公开(公告)号：US12282328B2

公开(公告)日：2025-04-22

申请号：US17150987

申请日：2021-01-15

Applicant: Aurora Operations, Inc.

Inventor： Raquel Urtasun ,  Bob Qingyuan Wei ,  Mengye Ren ,  Wenyuan Zeng ,  Ming Liang ,  Bin Yang

IPC: G05D1/00 ,  B60W60/00 ,  G06F18/213 ,  G06N20/00 ,  G06T17/05 ,  G06V10/82 ,  G06V20/56

Abstract: Systems and methods for generating attention masks are provided. In particular, a computing system can access sensor data and map data for an area around an autonomous vehicle. The computing system can generate a voxel grid representation of the sensor data and map data. The computing system can generate an attention mask based on the voxel grid representation. The computing system can generate, by using the voxel grid representation and the attention mask as input to a machine-learned model, an attention weighted feature map. The computing system can determine using the attention weighted feature map, a planning cost volume for an area around the autonomous vehicle. The computing system can select a trajectory for the autonomous vehicle based, at least in part, on the planning cost volume.

公开(公告)号：US12223734B2

公开(公告)日：2025-02-11

申请号：US17151001

申请日：2021-01-15

Applicant: Aurora Operations, Inc.

Inventor： Xuanyuan Tu ,  Raquel Urtasun ,  Tsun-Hsuan Wang ,  Sivabalan Manivasagam ,  Jingkang Wang ,  Mengye Ren

IPC: G06V20/56 ,  G01S17/931 ,  G05D1/00 ,  G06F18/21 ,  G06F18/24 ,  G06N20/00 ,  G06V10/764 ,  G06V10/82

Abstract: Systems and methods for vehicle-to-vehicle communications are provided. An adverse system can obtain sensor data representative of an environment proximate to a targeted system. The adverse system can generate an intermediate representation of the environment and a representation deviation for the intermediate representation. The representation deviation can be designed to disrupt a machine-learned model associated with the target system. The adverse system can communicate the intermediate representation modified by the representation deviation to the target system. The target system can train the machine-learned model associated with the target system to detect the modified intermediate representation. Detected modified intermediate representations can be discarded before disrupting the machine-learned model.

公开(公告)号：US12210344B2

公开(公告)日：2025-01-28

申请号：US18513119

申请日：2023-11-17

Applicant: Aurora Operations, Inc.

Inventor： Raquel Urtasun ,  Mengye Ren ,  Andrei Pokrovsky ,  Bin Yang

IPC: G05D1/00 ,  G01S17/86 ,  G01S17/89 ,  G01S17/931

Abstract: The present disclosure provides systems and methods that apply neural networks such as, for example, convolutional neural networks, to sparse imagery in an improved manner. For example, the systems and methods of the present disclosure can be included in or otherwise leveraged by an autonomous vehicle. In one example, a computing system can extract one or more relevant portions from imagery, where the relevant portions are less than an entirety of the imagery. The computing system can provide the relevant portions of the imagery to a machine-learned convolutional neural network and receive at least one prediction from the machine-learned convolutional neural network based at least in part on the one or more relevant portions of the imagery. Thus, the computing system can skip performing convolutions over regions of the imagery where the imagery is sparse and/or regions of the imagery that are not relevant to the prediction being sought.

公开(公告)号：US20250123620A1

公开(公告)日：2025-04-17

申请号：US18990684

申请日：2024-12-20

Applicant: Aurora Operations, Inc.

Inventor： Raquel Urtasun ,  Mengye Ren ,  Andrei Pokrovsky ,  Bin Yang

IPC: G05D1/00 ,  G01S17/86 ,  G01S17/89 ,  G01S17/931

Abstract: The present disclosure provides systems and methods that apply neural networks such as, for example, convolutional neural networks, to sparse imagery in an improved manner. For example, the systems and methods of the present disclosure can be included in or otherwise leveraged by an autonomous vehicle. In one example, a computing system can extract one or more relevant portions from imagery, where the relevant portions are less than an entirety of the imagery. The computing system can provide the relevant portions of the imagery to a machine-learned convolutional neural network and receive at least one prediction from the machine-learned convolutional neural network based at least in part on the one or more relevant portions of the imagery. Thus, the computing system can skip performing convolutions over regions of the imagery where the imagery is sparse and/or regions of the imagery that are not relevant to the prediction being sought.

公开(公告)号：US12214801B2

公开(公告)日：2025-02-04

申请号：US17528549

申请日：2021-11-17

Applicant: Aurora Operations, Inc.

Inventor： Jingkang Wang ,  Ava Alison Pun ,  Xuanyuan Tu ,  Mengye Ren ,  Abbas Sadat ,  Sergio Casas ,  Sivabalan Manivasagam ,  Raquel Urtasun

IPC: B60W60/00 ,  G06N3/086

Abstract: Techniques for generating testing data for an autonomous vehicle (AV) are described herein. A system can obtain sensor data descriptive of a traffic scenario. The traffic scenario can include a subject vehicle and actors in an environment. Additionally, the system can generate a perturbed trajectory for a first actor in the environment based on perturbation values. Moreover, the system can generate simulated sensor data. The simulated sensor data can include data descriptive of the perturbed trajectory for the first actor in the environment. Furthermore, the system can provide the simulated sensor data as input to an AV control system. The AV control system can be configured to process the simulated sensor data to generate an updated trajectory for the subject vehicle in the environment. Subsequently, the system can evaluate an adversarial loss function based on the updated trajectory for the subject vehicle to generate an adversarial loss value.

公开(公告)号：US12127085B2

公开(公告)日：2024-10-22

申请号：US17150998

申请日：2021-01-15

Applicant: Aurora Operations, Inc.

Inventor： Nicholas Baskar Vadivelu ,  Mengye Ren ,  Xuanyuan Tu ,  Raquel Urtasun ,  Jingkang Wang

IPC: H04W4/46 ,  B60W60/00 ,  G05D1/00 ,  G06F18/2415 ,  G06N20/00 ,  G06V20/56

CPC classification number: H04W4/46 ,  B60W60/00272 ,  B60W60/00276 ,  G05D1/0088 ,  G05D1/0274 ,  G06F18/2415 ,  G06N20/00 ,  G06V20/56

Abstract: Systems and methods for improved vehicle-to-vehicle communications are provided. A system can obtain sensor data depicting its surrounding environment and input the sensor data (or processed sensor data) to a machine-learned model to perceive its surrounding environment based on its location within the environment. The machine-learned model can generate an intermediate environmental representation that encodes features within the surrounding environment. The system can receive a number of different intermediate environmental representations and corresponding locations from various other systems, aggregate the representations based on the corresponding locations, and perceive its surrounding environment based on the aggregated representations. The system can determine relative poses between the each of the systems and an absolute pose for each system based on the representations. Each representation can be aggregated based on the relative or absolute poses of each system and weighted according to an estimated accuracy of the location corresponding to the representation.