Baidu USA LLC

Microsoft Technology Licensing, LLC

Baidu.com Times Technology (beijing) Co. Ltd.

Yahoo Assets LLC

Yu Wang

Qi Luo

Shu Jiang

Jinghao Miao

Jiangtao Hu

Jinyun Zhou

Jiaxuan Xu

Runxin He

Jiaming Tao

Jingao Wang

Jianfeng Gao

Yu Long Cao

Kecheng Xu

Xinying Song

Weiman Lin

Jinchao Li

Hui Su

Haiyuan Cao

Xiangquan Xiao

Shiyu Song

Jaideep Sarkar

Karan Srivastava

Longtao Lin

Ryen William White

Hoifung Poon

Zijian Zheng

Pengcheng He

An Yan

Prabhdeep Singh

Yu Hu

Xiao Huang

Hao Cheng

Li Zhou

Adrian Mihail Marinescu

Shen Huang

Jack Wilson Stokes, Iii

Xiaodong Liu

Yongzheng Zhang

Chao Ma

Weizhu Chen

Hongyi Sun

Kostas Tsioutsiouliklis

Chi-Yi Kuan

Donghyun Kim

Kuang Hu

Rao Shen

Eric Ray Hotinger

Steven M Lasker

Samarth Harish Shah

Liuqing Li

Yufeng Ma

Paul Ko

Bin Du

Sajay Antony

Shiwei Zhang

Brian Timothy Goff

Dongjiang You

Yu Cao

Siva Garudayagari

Cheng Deng

Dongyang Wang

Wei-Man Lin

Zongbao Feng

Shahina Ferdous

Ah Young Kim

Fanbin Bu

Shady Elasra

Described herein is a technique for establishing an overall company-level confidence score based on analyzing data from various data sources. The overall score is derived using a machine learned model that takes as input a set of sub-scores, with each sub-score representing a measure of how frequently a domain name occurs within the same data record as a company name. The model used to combine the several sub-scores is trained using training data that includes a variety of sub-scores for company and domain names that have already been verified to be associated with one another.

Technique for assessing trust based on company email domain data

This document relates to training of machine learning models. One example method involves providing a machine learning model having one or more mapping layers. The one or more mapping layers can include at least a first mapping layer configured to map components of pretraining examples into first representations in a space. The example method also includes performing a pretraining stage on the one or more mapping layers using the pretraining examples. The pretraining stage can include adding noise to the first representations of the components of the pretraining examples to obtain noise-adjusted first representations. The pretraining stage can also include performing a self-supervised learning process to pretrain the one or more mapping layers using at least the first representations of the training data items and the noise-adjusted first representations of the training data items.

Adversarial pretraining of machine learning models

The disclosed systems and methods provide a novel framework that provides mechanisms for a Deep & Cross Network (DCN) framework that performs distilled deep prediction for personalized stream ranking on portal websites. The disclosed framework is scalable to satisfy the much more stringent latency and computational requirements required by current network operating environments. The disclosed framework is able to dynamically evaluate and leverage live traffic on network sites in order to provide, update and maintain current recommendations for users as they traverse to a portal and when they navigate within the portal. The disclosed framework implements a DCN model(s) that is capable of being compressed into a model size for a unified optimization within a live traffic environment by combining knowledge distillation and model compression techniques. The disclosed framework is built as a light-weight deep learning model that can be served in production and perform on par with large models.

Computerized system and method for distilled deep prediction for personalized stream ranking

Systems, methods, and media for factoring localization uncertainty of an ADV into its planning and control process to increase the safety of the ADV. The uncertainty of the localization can be caused by sensor inaccuracy, map matching algorithm inaccuracy, and/or speed uncertainty. The localization uncertainty can have negative impact on trajectory planning and vehicle control. Embodiments described herein are intended to increase the safety of the ADV by considering localization uncertainty in trajectory planning and vehicle control. An exemplary method includes determining a confidence region for an ADV that is automatically driving on a road segment based on localization uncertainty and speed uncertainty; determining that an object is within the confidence region, and a probability of collision with the ADV based on a distance of the object to the ADV; and planning a trajectory based on the probability of collision, and controlling the ADV based on the probability of collision.

Control and planning with localization uncertainty

Disclosed are performance metrics for evaluating the accuracy of a dynamic model in predicting the trajectory of ADV when simulating the behavior of the ADV under the control commands. The performance metrics may indicate the degree of similarity between the predicted trajectory of the dynamic model and the actual trajectory of the vehicle when applied with identical control commands. The performance metrics measure deviations of the predicted trajectory of the dynamic model from the actual trajectory based on the ground truths. The performance metrics may include cumulative or mean absolute trajectory error, end-pose difference (ED), two-sigma defect rate (ε), the Hausdirff Distance (HAU), the longest common sub-sequence error (LCSS), or dynamic time warping (DTW). The two-sigma defect rate represents the ratio of the number of points with true location error falling out of the 2σ range of the predicted location error over the total number of points in the trajectory.

Dynamic model evaluation package for autonomous driving vehicles

According to various embodiments, systems, methods, and media for evaluating an open space planner in an autonomous vehicle are disclosed. In one embodiment, an exemplary method includes receiving, at a profiling application, a record file recorded by the ADV while driving in an open space using the open space planner, and a configuration file specifying parameters of the ADV; extracting planning messages and prediction messages from the record file, each extracted message being associated with the open space planner. The method further includes generating features from the planning message and the prediction messages in view of the specified parameters of the ADV; and calculating statistical metrics from the features. The statistical metrics are then provided to an automatic tuning framework for tuning the open space planner.

Open space planner profiling tool for autonomous vehicle

Computing system enhancements make container instantiation faster, reduce layer content storage demands, and make more container image formats available. A container instantiation location sends a container image pull request to a container registry, receives an image manifest, sends a layer mount request to the registry instead of a layer content download request, receives a layer mount, optionally repeats for additional layers, creates a union file system spanning the layers, and launches a container process based on the union file system without first downloading all the layer content. Inefficiencies and technical limitations of some other approaches are avoided, such as loopback mounts for snapshot expansion, creation or transmission of extra snapshots or extra container image clones, cluttering layer content with virtual machine settings, container system vendor lock-in, lack of container instantiation at a local system due to insufficient local storage, and lack of syscall optimization due to storage driver plugin usage.

Container instantiation with union file system layer mounts

A method of navigating an autonomous driving vehicle (ADV) includes determining a target function for an open space model based on one or more obstacles and map information within a proximity of the ADV, then iteratively performing first and second quadratic programming (QP) optimizations on the target function. Then, generating a second trajectory based on results of the first and second QP optimizations to control the ADV autonomously using the second trajectory. The first QP optimization is based on fixing a first set of variables of the target function. The second QP optimization is based on maximizing a sum of the distances from the ADV to each of the obstacles over a plurality of points of the first trajectory, and minimizing a difference between a target end-state of the ADV and a determined final state of the ADV using the first trajectory.

End dynamics and constraints relaxation algorithm on optimizing an open space trajectory

In one embodiment, a control command is generated with an MPC controller, the MPC controller including a cost function with weights associated with cost terms of the cost function. The control command is applied to a dynamic model of an autonomous driving vehicle (ADV) to simulate behavior of the ADV. One or more of the weights are based on evaluation of the dynamic model in response to the control command, resulting in an adjusted cost function of the MPC controller. Another control command is generated with the MPC controller having the adjusted cost function. This second control command can be used to effect movement of the ADV.

Learning based controller for autonomous driving

A simulation of an autonomous driving vehicle (ADV) includes capturing first data that includes a control command output by an autonomous vehicle controller of the ADV, and capturing second data that includes the control command being implemented at a control unit of the ADV. The control command, for example, a steering command, a braking command, or a throttle command, is implemented by the ADV to affect movement of the ADV. A latency model is determined based on comparing the first data with the second data, where the latency model defines time delay and/or amplitude difference between the first data and the second data. The latency model is applied in a virtual driving environment.

Dynamic model with actuation latency

In one embodiment, control of an autonomous driving vehicle (ADV) includes determining a current scenario of the ADV. Based on the scenario, a control algorithm is selected among a plurality of distinct control algorithms as the active control algorithm. One or more control commands are generated using the active control algorithm, based one or more target inputs. The control commands are applied to effect movement of the ADV.

Scenario based control of autonomous driving vehicle

Systems and methods are disclosed for optimizing values of a set of tunable parameters of an autonomous driving vehicle (ADV). The controllers can be a linear quadratic regular, a 'bicycle model,' a model-reference adaptive controller (MRAC) that reduces actuation latency in control subsystems such as steering, braking, and throttle, or other controller ('controllers'). An optimizer selects a set tunable parameters for the controllers. A task distribution system pairs each set of parameters with each of a plurality of simulated driving scenarios, and dispatches a task to the simulator to perform the simulation with the set of parameters. Each simulation is scored. A weighted score is generated from the simulation. The optimizer uses the weighted score as a target objective for a next iteration of the optimizer, for a fixed number of iterations. A physical real-world ADV is navigated using the optimized set of parameters for the controllers in the ADV.

Automatic parameter tuning framework for controllers used in autonomous driving vehicles

In one embodiment, a computer-implemented method of operating an autonomous driving vehicle (ADV) includes perceiving a driving environment surrounding the ADV based on sensor data obtained from one or more sensors mounted on the ADV, determining a driving scenario, in response to a driving decision based on the driving environment, applying a predetermined machine-learning model to data representing the driving environment and the driving scenario to generate a set of one or more driving parameters, and planning a trajectory to navigate the ADV using the set of the driving parameters according to the driving scenario through the driving environment.

Neural network approach for parameter learning to speed up planning for complex driving scenarios

Generally discussed herein are devices, systems, and methods for scheduling tasks to be completed by resources. A method can include identifying features of the task, the features including a time-dependent feature and a time-independent feature, the time-dependent feature indicating a time the task is more likely to be successfully completed by the resource, converting the features to feature values based on a predefined mapping of features to feature values in a first memory device, determining, by a gradient boost tree model and based on a first current time and the feature values, a likelihood the resource will successfully complete the task, and scheduling the task to be performed by the resource based on the determined likelihood.

Resource scheduling using machine learning

Growing community of inventors

Sunnyvale, CA, United States of America

Yu Wang