Asml Netherlands B.v.

Baidu USA LLC

Brion Technologies, Inc.

International Business Machines Corporation

Kla-tencor Technologies Corporation

Onetta, Inc.

Microsoft Technology Licensing, LLC

Oracle International Corporation

Facebook, Inc.

Kla Tencor Corporation

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

Yu Long Cao

Jun Ye

Luoqi Chen

Hanying Feng

Yen-Wen Lu

Wenjin Shao

Peng Liu

Shu Jiang

Xun Chen

Weiman Lin

Jiangtao Hu

Qi Luo

James Patrick Koonmen

Jinghao Miao

Tanveer Fathima Syeda-Mahmood

Yu Wang

Ronaldus Johannes Gijsbertus Goossens

Guang Yu Yang

Antoine Jean Bruguier

Jun Zhan

Yiqing Yang

Siyang Yu

Xuan Liu

Zhang Li

Stefan Hunsche

Yi Zou

Hua-Yu Liu

James Norman Wiley

Rafael C Howell

Simona Cohen

Ahmed El Harouni

Ya Luo

Deepika Kakrania

Roshni Biswas

Amram Abutbul

Longtao Lin

Youping Zhang

Jinyun Zhou

Kecheng Xu

Jiaxuan Xu

Marinus Aart Van Den Brink

Hongyi Sun

Hongliang Fei

Yufan Guo

David Alles

Shauh-Teh Juang

Xiangquan Xiao

Lih-Huah Yiin

Wei Fan

Yi Zhen

George Q Chen

Chaochun Liu

Richard Chun Ching Wang

Dawen Zhou

Ronaldus Johannes Gljsbertus Goossens

Shikhar Kwatra

Jun Zhu

Johannes Catharinus Hubertus Mulkens

Jingao Wang

Amit Agarwal

Jiaming Tao

Yang Zhou

Moshe E Preil

Hongzhi Wang

Jen-Shiang Wang

Chenxi Lin

Jing Su

Rohit Koul

R Fabian Pease

Guangqing Chen

Xin Fu

Shulong Tan

Wei Wei Wang

Venugopal Vellanki

Michael J Gassner

Zejun Lin

Wesley Reynolds

Alexis Blevins Baird

Jun Tao

Nan Du

Yusheng Xie

Szu-Hao Wu

Vineet Garg

Erheng Zhong

Szu Hao Wu

Dongguang Zhou

Ang Li

Raviraj Jain

Gilbert Leung

Bocong Liu

Lixin Feng

Fei Du

Martin Snajdr

Hua Cao

Luogi Chen

Zongbao Feng

Wei-Man Lin

Stanislas Hugo Louis Baron

Jorge Luis Padilla

Training methods and a mask correction method. One of the methods is for training a machine learning model configured to predict a post optical proximity correction (OPC) image for a mask. The method involves obtaining (i) a pre-OPC image associated with a design layout to be printed on a substrate, (ii) an image of one or more assist features for the mask associated with the design layout, and (iii) a reference post-OPC image of the design layout; and training the machine learning model using the pre-OPC image and the image of the one or more assist features as input such that a difference between the reference image and a predicted post-OPC image of the machine learning model is reduced.

Method for training machine learning model to determine optical proximity correction for mask

The present disclosure provides methods and techniques for evaluating and improving algorithms for autonomous driving planning and control (PNC), using one or more metrics (e.g., similarity scores) computed based on expert demonstrations. For example, the one or more metrics allow for improving PNC based on human, as opposed to or in addition to optimizing certain oversimplified properties, such as the least distance or time, as an objective. When driving in certain scenarios, such as taking a turn, people may drive in a distributed probability pattern instead of in a uniform line (e.g., different speeds and different curvatures at the same corner). As such, there can be more than one 'correct' control trajectory for an autonomous vehicle to perform in the same turn. Safety, comfort, speeds, and other criteria may lead to different preferences and judgment as to how well the controlled trajectory has been computed.

Distributional expert demonstrations for autonomous driving

According to some embodiments, systems, methods and media for dynamically generating scenario parameters for an autonomous driving vehicles (ADV) are described. In one embodiment, when an ADV enters a driving scenario, the ADV can invoke a map-based scenario checker to determine the type of scenario, and invokes a corresponding neural network model to generate a set of parameters for the scenario based on real-time environmental conditions (e.g., traffics) and vehicle status information (e.g., speed). The set of scenario parameters can be a set of extra constraints for configuring the ADV to drive in a driving mode corresponding to the scenario.

Dynamic scenario parameters for an autonomous driving vehicle

In one embodiment, an exemplary method includes the operations of receiving, at a profiling application, a record file recorded by the ADV for a driving scenario in an area, and a high definition map matching the area; extracting planning messages and perception messages from the record file; and aligning the planning message and the perception messages based on their timestamps. The method further includes calculating an individual performance score for each planning cycle of the ADV for the driving scenario based on the planning messages; calculating a weight for each planning cycle based on the perception messages and the high definition map; and then calculating a weighted score for the driving scenario based on individual performance scores and their corresponding weights.

Weighted planning trajectory profiling method for autonomous vehicle

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

An obstacle is detected based on sensor data obtained from a plurality of sensors of the ADV. A distribution of a plurality of positions of the obstacle at a point of time may be predicted. A range of positions of the plurality of positions of the obstacle may be determined based on a confidence level of the range. A modified shape with a modified length of the obstacle may be determined based on the range of positions of the obstacle. A trajectory of the ADV based on the modified shape with the modified length of the obstacle may be planned. The ADV may be controlled to drive according to the planned trajectory to drive safely to avoid a collision with the obstacle.

Planning under prediction with confidence region for an autonomous driving vehicle

According to some embodiments, described herein is a method and a system for guaranteeing safety at a control level of an ADV when at least a portion of a planned path generated by a planning module of the ADV is uncertain due to traffics and/or road condition changes. The planning module, when generating a path, also generate a confidence level of each segment of the path based on one or more of perception data, map information, or traffic rules. The confidence levels are decreasing further away from the ADV. When the control module of the ADV obtains the path and the associated confidence levels, the control module issue control commands to track only one or two segments whose confidence levels exceeds a threshold hold, and issue default control commands for the rest of the path.

Safety guaranteed control when planning with uncertainty

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

A method for calibrating a process model and training an inverse process model of a patterning process. The training method includes obtaining a first patterning device pattern from simulation of an inverse lithographic process that predicts a patterning device pattern based on a wafer target layout, receiving wafer data corresponding to a wafer exposed using the first patterning device pattern, and training an inverse process model configured to predict a second patterning device pattern using the wafer data related to the exposed wafer and the first patterning device pattern.

Machine learning based inverse optical proximity correction and process model calibration

A method including: obtaining a thin-mask transmission function of a patterning device and a M3D model for a lithographic process, wherein the thin-mask transmission function is a continuous transmission mask (CTM) and the M3D model at least represents a portion of M3D attributable to multiple edges of structures on the patterning device; determining a M3D mask transmission function of the patterning device by using the thin-mask transmission function and the M3D model; and determining an aerial image produced by the patterning device and the lithographic process, by using the M3D mask transmission function.

Methods of determining scattering of radiation by structures of finite thicknesses on a patterning device

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

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 for optimizing a controller of an autonomous driving vehicle (ADV) includes obtaining several samples, each sample having a set of parameters, iteratively performing, until a predetermined condition is satisfied: determining, for each sample, a score according to a configuration of the controller based on the set of parameters of the sample, applying a machine learning model to the samples and corresponding scores to determine a mean function and a variance function, producing a new sample as a minimum of a function of the mean function and the variance function with respect to an input space of the set of parameters, adding the new sample to the several samples, and outputting the new sample as an optimal sample, where parameters of the optimal sample are utilized to configure the controller to autonomously drive the ADV.

Bayesian Global optimization-based parameter tuning for vehicle motion controllers

Mechanisms are provided to implement a multi-layer analytics framework. The multi-layer analytics framework obtains a plurality of analytics from one or more analytics source computing systems, at least two analytics being written in different computer programming languages. The multi-layer analytics framework applies a wrapper to each of the analytics in the plurality of analytics to thereby generate wrapped analytics. The wrapper provides a unified interface for executing the analytics in the plurality of analytics regardless of the particular computer programming language used to create the analytics. The multi-layer analytics framework registers the wrapped analytics in an analytics registry, and executes an analytics pipeline comprising wrapped analytics in the analytics registry to perform an analytics operation based on the unified interface of the wrappers of the wrapped analytics.

Analytics framework for selection and execution of analytics in a distributed environment

Growing community of inventors

San Jose, CA, United States of America

Yu Long Cao