Plusai Limited

Applied Materials, Inc.

Advanced Micro Devices Corporation

Spansion Llc.

Ankur Agarwal

Shahid Rauf

Kartik Ramaswamy

Inderjot Singh Saggu

Andrew Nguyen

Ajit Balakrishna

Anurag Ganguli

Kenneth S Collins

James D Carducci

Douglas A Buchberger, Jr

Leonid Dorf

Richard Charles Fovell

Mianwei Zhou

Jason Andrew Kenney

Abhishek Bhatia

Rajinder Dhindsa

Vijay D Parkhe

Tza-Jing Gung

Michael D Willwerth

Samer Banna

Sergio Fukuda Shoji

Benjamin Schwarz

Katsumasa Kawasaki

Zhiqiang Guo

Martin Jeff Salinas

Duy D Nguyen

Chih-Hsun Hsu

Bryan Liao

Phillip Stout

Yashaswini Pattar

Minh Van Ngo

Angela Tai Hui

Thorsten B Lill

Valentin Nikolov Todorow

Ning Cheng

Robert Joseph Dingli

Timothy Patrick Daly, Jr

Zhigang Chen

James P Cruse

Jared Ahmad Lee

Ezra Robert Gold

Wenmei Li

Amol Ramesh Joshi

Haitao Wang

Aniruddha Pal

Wonseok Lee

Joseph Michael Martin

Tse-Chiang Wang

Norimitsu Takagi

This application is directed to augmenting training images used for generating vehicle driving models. A computer system obtains a first image of a road, identifies within the first image a drivable area of the road, obtains an image of a traffic safety object, and determines a detour path on the drivable area. The computer system determines positions of a plurality of traffic safety objects to be placed adjacent to the detour path, and generates a second image from the first image by adaptively overlaying a respective copy of the image of the traffic safety object at each of the positions of the plurality of traffic safety objects on the drivable area within the first image. The second image is added to a corpus of training images to be used by a machine learning system to generate a model for facilitating driving a vehicle (e.g., at least partial autonomously).

Data augmentation for detour path configuring

The present teaching relates to method, system, medium, and implementations for sensor calibration. An ego vehicle determines whether a sensor deployed thereon to facilitate autonomous driving needs to be calibrated and sends, if it is determined that the sensor needs to be calibrated, a request for assistance in collaborative calibration of the sensor to a center. The request includes at least a first position of the ego vehicle on the route and a first configuration of the sensor with respect to the ego vehicle. When the ego vehicle receives a calibration assistant package, which includes information associated with a collaborative means present along the route and to be used to assist the ego vehicle to calibrate the sensor, it identifies, based on the information, the collaborative means along the route when the ego vehicle is in a vicinity of the collaborative means in order to capture a target present on the collaborative means to enable calibration of the sensor.

System and method for collaborative calibration via landmark

In some embodiments, a method can include executing a first machine learning model to detect at least one lane in each image from a first set of images. The method can further include determining an estimate location of a vehicle for each image, based on localization data captured using at least one localization sensor disposed at the vehicle. The method can further include selecting lane geometry data for each image, from a map and based on the estimate location of the vehicle. The method can further include executing a localization model to generate a set of offset values for the first set of images based on the lane geometry data and the at least one lane in each image. The method can further include selecting a second set of images from the first set of images based on the set of offset values and a previously-determined offset threshold.

Methods and apparatus for automatic collection of under-represented data for improving a training of a machine learning model

The present teaching relates to method, system, medium, and implementations for sensor calibration. An ego vehicle determines whether a sensor deployed on the ego vehicle to facilitate autonomous driving of the ego vehicle needs to be calibrated and sends, if it is determined that the sensor needs to be calibrated, a request for assistance in collaborative calibration of the sensor, with a first position of the ego vehicle or a first configuration of the sensor with respect to the ego vehicle. When a response of the request is received, an assisting vehicle is indicated to travel to be near the ego vehicle to facilitate the calibration of the sensor by collaborating with the moving ego vehicle and the ego vehicle coordinates with the assisting vehicle to enable the sensor to acquire information of a target present on the assisting vehicle for the collaborative calibration of the sensor.

System and method for collaborative sensor calibration

Methods, systems, and non-transitory computer-readable media are configured to perform operations comprising determining an image of an environment. A drivable area and boundary information associated with the environment are determined based on the image. At least one boundary for navigation of the environment is generated based on the drivable area and boundary information.

Determining lanes from drivable area

Methods, systems, and non-transitory computer readable media are configured to perform operations comprising determining an occurrence of a sign in an environment of a vehicle; determining that at least a portion of the sign is unrecognized by a local machine learning model of the vehicle; and providing sensor data associated with the at least one portion of the sign to an operations center remote from the vehicle.

Automatic navigation based on traffic management vehicles and road signs

In some embodiments, an apparatus, comprises an unmanned vehicle configured to be disposed with a vehicle and a processor operatively coupled to the unmanned vehicle. The processor is configured to receive a first signal indicating a stop of the vehicle without a user request and a second signal indicating a location of the vehicle. The processor is configured to determine, based on the location of the vehicle, a target location in a pre-defined area surrounding the vehicle. The processor is configured to send a third signal to the unmanned vehicle to instruct the unmanned vehicle to move to the target location to alert other vehicles via a warning regarding occurrence of the stop.

Methods and apparatus for safety support with unmanned vehicles

Techniques are described for determining whether to process a job request. An example, method can include a device emitting a first pulse using a light detection and ranging (LIDAR) system coupled to an autonomous vehicle. The device can receive a first signal reflected off of an object. The device can emit a second pulse using the system, a threshold time interval being configured for the second laser pulse to hit the object in motion. The device can receive a second signal reflected off of the object. The device can determine a first time of flight information of the first signal and a second time of flight information of the second signal. The device can determine a velocity of the object based at least in part on the first time of flight information and the second time of flight information.

Velocity estimation using light detection and ranging (LIDAR) system

The present teaching relates to method, system, medium, and implementations for sensor calibration. A request is received from an ego vehicle in motion for assistance in collaborative calibration, when the ego vehicle determines that a sensor deployed thereon needs to be calibrated. The request includes at least one of a first position of the ego vehicle and a first configuration of the sensor with respect to the ego vehicle. An assisting vehicle is identified based on the first position, the first configuration, and a second position associated with the assisting vehicle and an assistance plan is generated in response to the request indicative of the assisting vehicle to travel to the ego vehicle to facilitate the calibration of the sensor by collaborating with the moving ego vehicle. Such generated assistance plan is then sent out to initiate the collaborative calibration.

System and method for coordinating collaborative sensor calibration

Embodiments described herein include a method of receiving, by a moving assisting vehicle, a calibration assistance request related to a moving ego vehicle that requested assistance in collaborative calibration of a sensor deployed on the moving ego vehicle. The method further includes analyzing the calibration assistance request to extract at least one of a schedule or an assistance route associated with the requested assistance. The method includes communicating with the moving ego vehicle about a desired location relative to the position of the moving ego vehicle for the moving assisting vehicle to be in order to assist the sensor to acquire information of a target present on the moving assisting vehicle. The method includes facilitating to drive the moving assisting vehicle to reach the desired location to achieve the collaborative calibration of the sensor on the moving ego vehicle.

System and method for assisting collaborative sensor calibration

Sensor data indicating a substantially 360 degree surrounding of a vehicle is received via a processor included in the vehicle. The sensor data is collected using multiple sensors included in the vehicle. Additionally, an acceptable response time range for a driver of the vehicle to perform an action with the vehicle is obtained, via the processor, based on the sensor data. Additionally, an actual response time for the driver to perform the action is determined, via the processor, based on CAN data collected from a CAN bus included in the vehicle, and not based on the sensor data. Additionally, a determination is made, via the processor, that the actual response time is not within the acceptable response time range. Additionally, a remedial action is caused, via the processor, to be performed in response to the determining that the actual response time is not within the acceptable response time range.

Determining acceptable responses for navigating a vehicle that accounts for external conditions of the vehicle

The present teaching relates to method and system related to providing an anti-tampering sensor assembly on a vehicle. An inertial measurement unit (IMU) is attached to a structure hosting at least one sensor to form a rigid integral structure, which is deployed on a vehicle with an installation pose to enable the at least one sensor therein to sense surrounding information to facilitate autonomous driving. The installation pose of the IMU is indicative of an installation pose of the at least one sensor. The IMU is configured to obtain, in accordance with a schedule, one or more measurements associated with its state, which can be used to enable detecting tampering of the rigid integral structure.

System and method for anti-tampering sensor assembly

The present teaching relates to method, system, medium, and implementations for detecting sensor tampering. Information is received from an inertial measurement unit (IMU) attached to a structure hosting at least one sensor. The information includes one or more measurements associated with the IMU. The at least one sensor is deployed on a vehicle for sensing surrounding information to facilitate autonomous driving. The one or more measurements are analyzed with respect to pre-determined criteria to detect tampering of the at least one sensor. When the tampering is detected, a response is generated to the event.

System and method for anti-tampering mechanism

Growing community of inventors

Mountain View, CA, United States of America

Ankur Agarwal