Baidu USA LLC

Omnivision Technologies, Inc.

Intel Corporation

Apple Inc.

Walmart Apollo, LLC

Wei Wei Wang

Qi Luo

Kecheng Xu

Hongyi Sun

Zejun Lin

Wesley Reynolds

Xin Ru Wang

Jiangtao Hu

Rui Wang

Jinghao Miao

Qingwei Shan

Zhiyong Zhan

Qingfei Chen

Kenny Geng

Yu Long Cao

Feng Quan Liu

Paul Argus Parks

Weiman Lin

Stefano Giaconi

Mingming Xu

Zebin Chen

Patrick J Czarnota

Edmund Leung Ma

Jufang Wang

Wenke Zhang

Linglong Zhu

Ming Zhang

Systems and methods for generating labelled audio data and onboard validation of the labelled audio data utilizing an autonomous driving vehicle (ADV) while the ADV is operating within a driving environment are disclosed. The method includes recording a sound emitted by an object within the driving environment of the ADV, and converting the recorded sound into audio samples. The method further includes labelling the audio samples, and refining the labelled audio samples to produce refined labelled audio data. The refined labelled audio data is utilized to subsequently train a machine learning algorithm to recognize a sound source during autonomous driving of the ADV. The method further includes generating a performance profile of the refined labelled audio data based at least on the audio samples, a position of the object, and a relative direction of the object. The position of the object and the relative direction of the object are determined by a perception system of the ADV.

Audio logging for model training and onboard validation utilizing autonomous driving vehicle

An imaging device includes groupings of photodiodes having four photodiodes. A transfer transistor is between each photodiode and a floating diffusion. Each floating diffusion is coupled to up to two photodiodes per grouping at a time through transfer transistors. A buffer transistor is coupled to each floating diffusion. The buffer transistors may be in a first or second grouping of buffer transistors. A first bit line is coupled to up to two buffer transistors of the first grouping and a second bit line is coupled to up to two buffer transistors of the second grouping of buffer transistors at a time. A color filter array including a plurality of groupings of color filters is disposed over respective photodiodes of the photodiode array, wherein each grouping of color filters includes four color filters having a same color, wherein each grouping of color filters overlaps two groupings of photodiodes.

Image sensor with shifted color filter array pattern and bit line pairs

According to various embodiments, systems, methods, and mediums for operating an autonomous driving vehicles (ADV) are described. The embodiments use a number of machine learning models to extract features individually from audio data and visual data captured by sensors mounted on the ADV, and then to fuse these extracted features to create a concatenated feature vectors. The concatenated feature vector is provided to a multiplayer perceptron (MLP) as input to generate a detection result related to the presence of an emergency vehicle in the surrounding environment. The detection result can be used by the ADV to take appropriate actions to comply with the local traffic rules.

Machine learning model to fuse emergency vehicle audio and visual detection

A micro device transfer tool and methods of operation are described. In an embodiment, the micro device transfer tool includes an articulating transfer head assembly capable of six degrees of motion. A miniatured camera assembly may be secured near the point of contact for the articulating transfer head assembly to aid in system alignment. In an embodiment, an encoder system is described for alignment of a micro pick up array and target substrate using complementary concentric grating patterns. In an embodiment a miniaturized position sensor design is described for sensing position of various system components during alignment or pick and place processes.

Micro device mass transfer tool

Systems and methods for sound source detection and localization utilizing an autonomous driving vehicle (ADV) are disclosed. The method includes receiving audio data from a number of audio sensors mounted on the ADV. The audio data comprises sounds captured by the audio sensors and emitted by one or more sound sources. Based on the received audio data, the method further includes determining a number of sound source information. Each sound source information comprises a confidence score associated with an existence of a specific sound. The method further includes generating a data representation to report whether there exists the specific sound within the driving environment of the ADV. The data representation comprises the determined sound source information. The received audio data and the generated data representation are utilized to subsequently train a machine learning algorithm to recognize the specific sound source during autonomous driving of the ADV in real-time.

Sound source detection and localization for autonomous driving vehicle

This disclosure relates to systems and methods for executing a caching procedure for a web cache that is utilized by an electronic platform storing a plurality of clusters. The caching procedure may comprise retrieving a priming cutoff parameter that identifies a timeframe, utilizing the priming cutoff parameter to identify a subset of clusters that have been added or updated within the timeframe, and priming the web cache with content associated with the subset of clusters. The content in the web cache can be transmitted in response to receiving requests for the content. Other embodiments are disclosed herein.

Web caching techniques

Some embodiments include apparatuses and methods having a receiver unit included in a die and a measurement unit included in the die. The receiver unit includes a sampler to sample a first signal based on timing of a first clock signal to generate a second signal. The measurement unit is arranged to sample the first signal based on timing of a second clock signal to provide information for generation of a graph presenting an eye scan of the first signal. The second clock signal has a frequency asynchronous with a frequency of the first clock signal.

Asynchronous on-die eye scope

Growing community of inventors

Sunnyvale, CA, United States of America

Wei Wei Wang