Huawei Technologies Co., Limited

Nvidia Corporation

Deepmap Inc.

International Business Machines Corporation

Apple Inc.

Csr Technology Inc.

Marvell Asia Pte., Ltd.

Infinera Corporation

Chen Chen

Mark Damon Wheeler

Zhuhong Zhang

Chuandong Li

Reid T Copeland

Di Zeng

Christopher Eugene Bowler

Tarique M Islam

Raúl E Silvera

Tommy U Hoffner

Gregory William Coombe

Ronghua Zhang

Zhiping Jiang

Derik Schroeter

Liang Zou

Jeffrey Minoru Adachi

Lei He

Greg Coombe

Jeff Adachi

Xia Li

Wei Hsin Yao

Hopil Bae

Derek Keith Shaeffer

Chong Chen

Wei Xiong

David R Auld

Henry C Jen

Xiang Lu

Rene Marcel Schmogrow

Srikrishna Sridhar

Mahdi Zamani

Jason T S Lam

Majid Gharghi

Xiaodi Ke

Lei Guo

Stanley B Wang

Nikhil Acharya

Abdelrahman Abdelhamed

Pablo Moreno Galbis

Sunmin Jang

Jin Chen

Yongjie Jiang

Hari P Paudel

Feng Yu

Gustav M Larsson

Ling Zhang

Hao Hai Ma

Ziqiang Huang

Dongwon Kwon

Shengzhe Jiao

Haojiang Zhou

Gerard Kuang-Chang Yeh

Jiangkun Liu

Jialin Liu

Arjunsimha Chaturbhuj

Ruofan Zhang

A display system may include a plurality of light emitting diodes to emit light, where each of the light emitting diodes are connected in parallel and a plurality of first switches respectively coupled to the plurality of light emitting diodes. A first switch of the plurality of first switches is coupled in parallel to a first light emitting diode. The first switch may short the first light emitting diode of the plurality of light emitting diodes through a defined resistor having a defined value that therefore draws a defined short circuit current.

Systems and methods for mitigating micro-LED failures

A receiver includes an optical front-end and digital circuitry. The optical front-end is configured to receive an optical signal including first and second optical signal components having first and second polarizations and modulated with symbols at a symbol rate. The digital circuitry is configured to derive first and second digital signals representing the first and second optical signal components having the first and second polarizations. The digital circuitry includes a clock detector configured to calculate correlation terms, the correlation terms being calculated in a frequency-domain with a frequency offset commensurate with the symbol rate. The clock detector is configured to recover a clock signal of the symbols by (i) summing selected pairs of the correlation terms, and (ii) calculating or estimating a sum-of-squares of the summed pairs.

Coherent receiver with polarization diversity clock detection

A high-definition map system receives sensor data from vehicles travelling along routes and combines the data to generate a high definition map for use in driving vehicles, for example, for guiding autonomous vehicles. A pose graph is built from the collected data, each pose representing location and orientation of a vehicle. The pose graph is optimized to minimize constraints between poses. Points associated with surface are assigned a confidence measure determined using a measure of hardness/softness of the surface. A machine-learning-based result filter detects bad alignment results and prevents them from being entered in the subsequent global pose optimization. The alignment framework is parallelizable for execution using a parallel/distributed architecture. Alignment hot spots are detected for further verification and improvement. The system supports incremental updates, thereby allowing refinements of sub-graphs for incrementally improving the high-definition map for keeping it up to date.

Detection of misalignment hotspots for high definition maps for navigating autonomous vehicles

A method includes obtaining first user input identifying at least one LIDAR point in a set of LIDAR points associated with an object in an image, and obtaining second user input identifying the object in the image. The method may also include generating a constraint on a relationship between a LIDAR sensor used to capture the set of LIDAR points and a camera used to capture the image. The method may additionally include reducing a cost associated with the LIDAR point being inconsistent with the object in the image subject to the constraint.

Interactive sensor calibration for autonomous vehicles

According to an aspect of an embodiment, operations may comprise obtaining a pose graph that comprises a plurality of nodes. The operations may also comprise dividing the pose graph into a plurality of pose subgraphs, each pose subgraph comprising one or more respective pose subgraph interior nodes and one or more respective pose subgraph boundary nodes. The operations may also comprise generating one or more boundary subgraphs based on the plurality of pose subgraphs, each of the one or more boundary subgraphs comprising one or more respective boundary subgraph boundary nodes and comprising one or more respective boundary subgraph interior nodes. The operations may also comprise obtaining an optimized pose graph by performing a pose graph optimization. The pose graph optimization may comprise performing a pose subgraph optimization of the plurality of pose subgraphs and performing a boundary subgraph optimization of the plurality of boundary subgraphs.

Distributed processing of pose graphs for generating high definition maps for navigating autonomous vehicles

A vehicle computing system validates location data received from a Global Navigation Satellite System receiver with other sensor data. In one embodiment, the system calculates velocities with the location data and the other sensor data. The system generates a probabilistic model for velocity with a velocity calculated with location data and variance associated with the location data. The system determines a confidence score by applying the probabilistic model to one or more of the velocities calculated with other sensor data. In another embodiment, the system implements a machine learning model that considers features extracted from the sensor data. The system generates a feature vector for the location data and determines a confidence score for the location data by applying the machine learning model to the feature vector. Based on the confidence score, the system can validate the location data. The validated location data is useful for navigation and map updates.

Augmentation of global navigation satellite system based data

Electronic devices, methods, and non-transitory program storage devices for leveraging machine learning to perform improved image fusion and/or noise reduction are disclosed. When a capture request is received, a neural network may be used to perform fusion and denoising operations on a first set of captured input images. According to some embodiments, the neural network's architecture comprises: a first plurality of network layers configured to compute optical flow information between the first set of input images; a second plurality of network layers configured to perform, at least in part, the fusion and denoising operations on the first set of input images; and a third plurality of skip connections between layers of the second plurality of network layers, wherein parameters for each skip connection of the third plurality of skip connections are warped based on at least part of the optical flow information computed by the first plurality of network layers.

Burst image fusion and denoising using end-to-end deep neural networks

An autonomous vehicle system removes ephemeral points from lidar samples. The system receives a plurality of light detection and ranging (lidar) samples captured by a lidar sensor. Along with the lidar samples, the system receives an aligned pose and an unwinding transform for each of the lidar samples. The system determines one or more occupied voxel cells in a three-dimensional (3D) space using the lidar samples, their aligned poses, and their unwinding transforms. The system identifies occupied voxel cells representative of noise associated with motion of an object relative to the lidar sensor. The system filters the occupied voxel cells by removing the cells representative of noise. The system inputs the filtered occupied voxel cells in a 3D map comprising voxel cells, e.g., during the map generation and/or a map update.

Removal of ephemeral points from point cloud of a high-definition map for navigating autonomous vehicles

A system align point clouds obtained by sensors of a vehicle using kinematic iterative closest point with integrated motions estimates. The system receives lidar scans from a lidar mounted on the vehicle. The system derives point clouds from the lidar scan data. The system iteratively determines velocity parameters that minimize an aggregate measure of distance between corresponding points of the plurality of pairs of points. The system iteratively improves the velocity parameters. The system uses the velocity parameters for various purposes including for building high definition maps used for navigating the vehicle.

Iterative closest point process based on lidar with integrated motion estimation for high definition maps

Validation of global navigation satellite system location data with other sensor data

A high-definition map system receives sensor data from vehicles travelling along routes and combines the data to generate a high definition map for use in driving vehicles, for example, for guiding autonomous vehicles. A pose graph is built from the collected data, each pose representing location and orientation of a vehicle. The pose graph is optimized to minimize constraints between poses. Points associated with surface are assigned a confidence measure determined using a measure of hardness/softness of the surface. A machine-learning-based result filter detects bad alignment results and prevents them from being entered in the subsequent global pose optimization. The alignment framework is parallelizable for execution using a parallel/distributed architecture. Alignment hot spots are detected for further verification and improvement. The system supports incremental updates, thereby allowing refinements of subgraphs for incrementally improving the high-definition map for keeping it up to date.

A high-definition map system receives sensor data from vehicles traveling along routes and combines the data to generate a high definition map for use in driving vehicles, for example, for guiding autonomous vehicles. A pose graph is built from the collected data, each pose representing location and orientation of a vehicle. The pose graph is optimized to minimize constraints between poses. Points associated with surface are assigned a confidence measure determined using a measure of hardness/softness of the surface. A machine-learning-based result filter detects bad alignment results and prevents them from being entered in the subsequent global pose optimization. The alignment framework is parallelizable for execution using a parallel/distributed architecture. Alignment hot spots are detected for further verification and improvement. The system supports incremental updates, thereby allowing refinements of subgraphs for incrementally improving the high-definition map for keeping it up to date.

Classification of surfaces as hard/soft for combining data captured by autonomous vehicles for generating high definition maps

Resource scheduling methods and a resource manager define leaf buckets, each of which have coordinates in corresponding dimensions in a coordinate space. Nodes having resource values in the corresponding dimensions are mapped on the leaf buckets. A task request identifies candidate nodes and requested resources that relate to the coordinate space dimensions. The leaf buckets are scanned to find a fittable node among the list of candidates. A result of the scan is stored in a positive or a negative cache entry. When a next task request of a same task group is received, scanning of the leaf buckets is performed using information from the positive or negative cache entry. For a positive cache entry, the scanning is initiated from a point where the previous scanning has ended. For a negative cache entry, the scanning ignores nodes that were found not to contain the requested resources in the previous scanning.

Resource scheduling methods using positive and negative caching, and resource manager implementing the methods

As an autonomous vehicle moves through a local area, pairwise alignment may be performed to calculate changes in the pose of the vehicle between different points in time. The vehicle comprises an imaging system configured to capture image frames depicting a portion of the surrounding area. Features are identified from the captured image frames, and a 3-D location is determined for each identified feature. The features of different image frames corresponding to different points in time are analyzed to determine a transformation in the pose of the vehicle during the time period between the image frames. The determined poses of the vehicle are used to generate an HD map of the local area.

Visual odometry and pairwise alignment for high definition map creation

Growing community of inventors

San Jose, CA, United States of America

Chen Chen