公开(公告)号：US12116015B2

公开(公告)日：2024-10-15

申请号：US17528559

申请日：2021-11-17

申请人： Aurora Operations, Inc.

发明人： Bin Yang ,  Ming Liang ,  Wenyuan Zeng ,  Min Bai ,  Raquel Urtasun

IPC分类号： B60W60/00 ,  G05D1/00 ,  G06N20/00

CPC分类号： B60W60/0027 ,  G05D1/0221 ,  G05D1/0231 ,  G06N20/00 ,  B60W2554/4026 ,  B60W2554/4029 ,  B60W2554/4041 ,  B60W2554/4044 ,  B60W2556/45

摘要： Techniques for improving the performance of an autonomous vehicle (AV) by automatically annotating objects surrounding the AV are described herein. A system can obtain sensor data from a sensor coupled to the AV and generate an initial object trajectory for an object using the sensor data. Additionally, the system can determine a fixed value for the object size of the object based on the initial object trajectory. Moreover, the system can generate an updated initial object trajectory, wherein the object size corresponds to the fixed value. Furthermore, the system can determine, based on the sensor data and the updated initial object trajectory, a refined object trajectory. Subsequently, the system can generate a multi-dimensional label for the object based on the refined object trajectory. A motion plan for controlling the AV can be generated based on the multi-dimensional label.

公开(公告)号：US12103554B2

公开(公告)日：2024-10-01

申请号：US17150982

申请日：2021-01-15

申请人： Aurora Operations, Inc.

发明人： Raquel Urtasun ,  Kelvin Ka Wing Wong ,  Qiang Zhang ,  Bin Yang ,  Ming Liang ,  Renjie Liao

IPC分类号： B60W60/00 ,  B60W50/00 ,  G06F30/27 ,  G06N3/08

CPC分类号： B60W60/001 ,  B60W50/00 ,  G06F30/27 ,  G06N3/08 ,  B60W2050/0019 ,  B60W2050/0083

摘要： Systems and methods of the present disclosure are directed to a method. The method can include obtaining simplified scenario data associated with a simulated scenario. The method can include determining, using a machine-learned perception-prediction simulation model, a simulated perception-prediction output based at least in part on the simplified scenario data. The method can include evaluating a loss function comprising a perception loss term and a prediction loss term. The method can include adjusting one or more parameters of the machine-learned perception-prediction simulation model based at least in part on the loss function.