公开(公告)号：US20240329961A1

公开(公告)日：2024-10-03

申请号：US18190706

申请日：2023-03-27

Applicant: Apollo Autonomous Driving USA LLC

Inventor： Qiang WANG ,  Manjiang ZHANG ,  Congshi HUANG

IPC: G06F8/65 ,  G06F11/14 ,  H04L67/12

CPC classification number: G06F8/65 ,  G06F11/1433 ,  H04L67/12 ,  B60W60/001 ,  B60W2556/45 ,  G06F2201/865

Abstract: An autonomous driving vehicle (ADV) includes a computer network that includes at least one wireless gateway. The ADV also includes a plurality of network components coupled to the computer network, the plurality of network components including a target device. The ADV also includes a processor, configured to receive, through the at least one wireless gateway, a firmware to be installed on the target device, and to direct the firmware to the target device through a first network path of the computer network. In response to detecting a failure to update the target device with the firmware, the processor directs the firmware to the target device through a second network path of the computer network.

公开(公告)号：US20250094854A1

公开(公告)日：2025-03-20

申请号：US18005904

申请日：2022-11-28

Applicant: Apollo Autonomous Driving USA LLC ,  Beijing Baidu Netcom Science Technology Co., Ltd.

Inventor： Haofeng KOU ,  Xiaoyi ZHU ,  Manjiang ZHANG ,  Helen K. PAN

IPC: G06N20/00 ,  B60W60/00

Abstract: A vehicle operating system (VOS) in an autonomous driving vehicle (ADV) can communicate with a cloud platform to automatically train AI models. The VOS collects real-time data from the ADV, and generates inference data based on the real-time data using a teacher edge model of an AI model and generates second inference data based on the real-time data using a student edge model of the AI model. The VOS then obtains one or more differences between the first inference data and the second inference data, and retrains the student edge model of the AI model based on the one or more differences. Both real-time data and the retrained student edge model are uploaded to a cloud platform for use in upgrading the student edge model and the teacher edge model on the cloud platform. The upgraded teacher edge model and the student edge model can be redeployed over-the-air (OTA) through a software define process. The above process of training AI models can be repeated in a closed-loop automatically without user intervention.

公开(公告)号：US20240166241A1

公开(公告)日：2024-05-23

申请号：US18058009

申请日：2022-11-22

Applicant: Apollo Autonomous Driving USA LLC

Inventor： Manjiang ZHANG ,  Haofeng KOU

IPC: B60W60/00 ,  B60W50/029

CPC classification number: B60W60/0015 ,  B60W50/029 ,  B60W2050/0062

Abstract: The present disclosure provides a system and method that retrieves a plurality of logic blocks and a plurality of safety levels corresponding to the plurality of logic blocks. The system and method determines, by a processor, which of the plurality of logic blocks require one or more redundant logic blocks based on their corresponding safety level. The system and method produces a logic design based on the plurality of logic blocks and the one or more redundant logic blocks.

公开(公告)号：US20240210939A1

公开(公告)日：2024-06-27

申请号：US18069676

申请日：2022-12-21

Applicant: Apollo Autonomous Driving USA LLC

Inventor： Guoli SHU ,  Manjiang ZHANG

IPC: G05D1/00 ,  B60W60/00 ,  G06V20/56

CPC classification number: G05D1/0038 ,  B60W60/001 ,  G06V20/56 ,  B60W2420/42

Abstract: A cost-latency balanced method of processing camera image data in an autonomous driving vehicle (ADV) is described. The ADV includes a main compute unit coupled to an FPGA unit and a graphical processing unit (GPU). The method includes receiving, by the main compute unit, a full raw image data and a partial compressed image data from the FPGA unit, the full raw image being raw image data captured by all cameras mounted on the ADV, and the partial compressed image data being compressed from a partial raw image data captured by a subset of the cameras mounted on the ADV. The method further includes transmitting the partial compressed image data to a remote driving operation center; and consuming the full raw image data for environment perception, and the full raw image data is also compressed into a full compressed image data by the GPU for use in offline processing.