公开(公告)号：US20220333944A1

公开(公告)日：2022-10-20

申请号：US17763230

申请日：2019-12-27

Applicant: Intel Corporation ,  Mobileye Vision Technologies LTD.

Inventor： Yuqing HOU ,  Xiaolong LIU ,  Ignacio J. ALVAREZ ,  Xiangbin WU

IPC: G01C21/34 ,  G01C21/36

Abstract: Provided is a device and a method for route planning. The route planning device (100) may include a data interface (128) coupled to a road and traffic data source (160); a user interface (170) configured to display a map and receive a route planning request from a user, the route planning request including a line of interest on the map; a processor (110) coupled to the data interface (128) and the user interface (170). The processor (110) may be configured to identify the line of interest in response to the route planning request; acquire, via the data interface (128), road and traffic information associated with the line of interest from the road and traffic data source (160); and calculate, based on the acquired road and traffic information, a navigation route that matches or corresponds to the line of interest and meets or satisfies predefined road and traffic constraints.

公开(公告)号：US20210302168A1

公开(公告)日：2021-09-30

申请号：US16831104

申请日：2020-03-26

Applicant: Intel Corporation

Inventor： Xiaolong LIU ,  Zhigang WANG ,  Dawei WANG ,  Haitao JI ,  Qianying ZHU ,  Fei LI ,  Ignacio J ALVAREZ

IPC: G01C21/30 ,  G06K9/00 ,  G01C21/36

Abstract: The present disclosure relates to real-time localization error correction of an autonomous vehicle (AV). A processor for real-time localization error correction of the AV is provided. The processor is configured to retrieve a reference landmark around the AV from a map aggregating server (MAS), wherein the AV is configured to interact with the MAS for real-time localization; detect, in real time, a ground truth landmark corresponding to the reference landmark, according to image data captured by one or more image capture devices installed on the AV; and determine a deviation between the ground truth landmark and the reference landmark as a real-time correction value for the real-time localization of the AV.

公开(公告)号：US20240013047A1

公开(公告)日：2024-01-11

申请号：US18252231

申请日：2020-12-24

Applicant: Intel Corporation

Inventor： Dongqi CAI ,  Anbang YAO ,  Yurong CHEN ,  Xiaolong LIU

IPC: G06N3/08

CPC classification number: G06N3/08 ,  G06V10/7715

Abstract: Dynamic conditional pooling for neural network processing is disclosed. An example of a storage medium includes instructions for receiving an input at a convolutional layer of a convolutional neural network (CNN); receiving an input sample at a pooling stage of the convolutional layer; generating a plurality of soft weights based on the input sample; performing conditional aggregation on the input sample utilizing the plurality of soft weights to generate an aggregated value; and performing conditional normalization on the aggregated value to generate an output for the convolutional layer.

公开(公告)号：US20230368493A1

公开(公告)日：2023-11-16

申请号：US18030024

申请日：2020-11-13

Applicant: Intel Corporation

Inventor： Yuqing HOU ,  Xiaolong LIU ,  Anbang YAO ,  Yurong CHEN

IPC: G06V10/764 ,  G06V10/82 ,  G06V10/776

CPC classification number: G06V10/764 ,  G06V10/82 ,  G06V10/776

Abstract: A method and system of image hashing object detection for image processing are provided. The method comprises the following steps: obtaining image head class input data and image tail class input data differentiated from the head class input data and respectively of two images each of an object to be classified; respectively inputting the head and tail class input data into two separate parallel representation neural networks being trained to respectively generate head and tail features, wherein the representation neural networks share at least some representation weights used to form the head and tail features; inputting the head and tail features into at least one classifier neural network to generate class-related data; generating a class-balanced loss of at least one of the classes of the class-related data comprising factoring an effective number of samples of individual classes; and rebalancing an output sample distribution among the classes at the representation neural networks, classifier neural networks, or both by using the class-balanced loss.