Gm Global Technolgoy Operations, Inc.

Xinyu Du

Shengbing Jiang

Yao Hu

Hao Yu

Yilu Zhang

Wen-Chiao Lin

Wende Zhang

Binbin Li

Atul Nagose

Mutasim Abdurrahman Salman

Paul Edward Krajewski

Lyall Kenneth Winger

Kevin A Cansiani

Aaron B Bloom

Joshua J Sanchez

Chaitanya Sankavaram

Infane Lowe

Youssef A Ghoneim

Zhongyu Yang

Chandra S Namuduri

Fan Bai

Suresh Gopalakrishnan

Varsha K Sadekar

Brian K Saylor

James Morrison

Qi Zhang

Xiaoyu Huang

Xiaofeng Mao

Rod Niner

Natalie Ann Wienckowski

David Gumpert

Guanlun He

Dongyi Zhou

Lichao Mai

Joseph F Szczerba

Shuqing Zeng

Bo Yu

Jinsong Wang

Muhammad Hussain Alvi

Shige Wang

Jan Henning Aase

Shiming Duan

Mark N Howell

Xidong Tang

Pulak Bandyopadhyay

Satnam Singh

Steven W Holland

Azeem Sarwar

Robert A De Stefano

Mark J Rychlinski

Timothy D Julson

Unmesh Dutta Bordoloi

Jonathan O Conell

Gary W Taraski

Mutasim A Salman

Rasoul Salehi

Mateusz M Nowak

Mohsen Khalili

Alexander M Allan

Daniel Demitrish

Yat-chung Tang

Issa Aljanabi

Zifeng Peng

Yan Chen

Timothy J Romelhardt

Shuo Huang

Eric J Baur

Sidi Lu

Gregory G Tuayev-Deane

Ao Li

Xiaobo Guo

Aaron D Motyl

Gavril Cret

Jianshe Feng

Arvind Sharma

Kevin M Corr

A vehicle includes a system and method of operating the vehicle. The system includes a camera, a map database and a processor. The camera obtains camera data of a location of a road being traversed by the vehicle. The map database provides map data of the location of the road. The processor determines a first curvature of the road at the location from the camera data, determines a second curvature of the road at the location from the map data, identifies a mismatch between the first curvature and the second curvature at the location of the road, generates a case report for one of the map data and the camera data at the location upon occurrence of the mismatch, and adjusts one of the map data and a confidence level in the camera data for the location based on the case report.

Self adaptive enhancement for automated driving with map and camera issues

A vehicle includes a system performing a method of controlling an operation of the vehicle during a tire blowout. The system includes a vehicle dynamics sensor for detecting a dynamic parameter of the vehicle, tire sensors for detecting wheel speeds of wheels of the vehicle, and a processor. The processor is configured to receive a velocity of the vehicle from the vehicle dynamics sensor and the wheel speeds from the tire sensors, determine a diagonal product ratio of the wheel speeds, compare the diagonal product ratio to a range to detect an occurrence of a tire blowout, identify a location of the tire blowout from a wheel speed ratio based on wheels that are diagonally opposite each other, and control the operation of the vehicle in response to the tire blowout.

System and method for tire blowout detection using wheel speeds

A system and method for diagnosing vehicle battery health using electrode potential estimation is presented. A first set of anode electrode potentials and a first set of cathode electrode potentials of a pristine vehicle battery cell are determined at a first charge rate. A second set of anode electrode potentials and a second set of cathode electrode potentials of a pristine vehicle battery cell are determined at a second charge rate. A determination of a set of optimized health indicators are made based on the first set of anode electrode potentials, the first set of cathode electrode potentials, the second set of anode electrode potentials, and the second set of cathode electrode potentials. Testing a vehicle battery is administered, based on the optimized health indicators, after a first number of cycles to determine a faulty one or more cells of the vehicle battery.

Vehicle battery health diagnostics by electrode potential estimation

A computer-implemented method for aligning a sensor to reference coordinate system includes initiating a plurality of threads, each thread executes simultaneously and independent of each other. A first thread parses data received from the sensor and stores the parsed data in a data buffer. A second thread computes an alignment transformation using the parsed data to determine alignment between the sensor and the reference coordinate system. The computing includes checking that the data buffer contains at least predetermined amount of data. If at least the predetermined amount of data exists, an intermediate result is computed using the parsed data in the data buffer; otherwise, the second thread waits for the first thread to add more data to the data buffer. The second thread outputs the intermediate result into the data buffer. A third thread outputs the alignment transformation, in response to completion of alignment computations.

Vehicle-onboard computing architecture for sensor alignment

A vehicle, and a system and method of navigating the vehicle. The system includes a camera and a processor. The camera obtains an image of a road upon which the vehicle is moving. The processor is configured to extract a feature of the road from the image, perform a lane detection algorithm to detect a set of lane markers in the road using the image and the feature, and move the vehicle along the road by tracking the set of lane markers.

Mitigation strategies for lane marking misdetection

A system in a vehicle includes a lidar system to obtain lidar data in a lidar coordinate system, and processing circuitry to obtain the lidar data and localization data. The localization data indicates a location and orientation of the vehicle. The processing circuitry automatically determines an alignment state resulting in a lidar-to-vehicle transformation matrix that projects the lidar data from the lidar coordinate system to a vehicle coordinate system to provide lidar-to-vehicle data. The alignment state is determined using the localization data.

Automatic detection of lidar to vehicle alignment state using localization data

A vehicle includes a system performing a method of operating a camera for the vehicle. The camera is configured to capture an image. The processor is configured to query the camera to obtain an intrinsic parameter of the camera, estimate a unified parameter based on the intrinsic parameter of the camera, and perform at least one application on the image using the unified parameter.

Automatic unification of camera intrinsic model

A vision system for a vehicle includes a camera configured to generate one of distorted and undistorted images. An alignment system is configured to generate a region of interest (ROI) for the camera, generate a bird's eye view (BEV) homography to convert undistorted points in the ROI to BEV points, generate a BEV image based on the one of distorted and undistorted images and one of the BEV homography and an updated BEV homography, adaptively update the BEV homography to generate the updated BEV homography, detect current locations of features in the BEV image, predict future locations of the features for a next BEV image, compare the current locations of the features in the BEV image with prior predicted locations, and convert the BEV points in the BEV image to an undistorted points in a perspective undistorted image using an inverse of the updated BEV homography.

Vehicle vision system including camera to ground alignment using bird's eye view image

An electrical connector is provided. The electrical connector includes a first housing having an outer surface and a second housing configured to receive the first housing, wherein the second housing includes an inner surface configured to contact the outer surface of the first housing. The electrical connector also includes a plurality of sensors configured to monitor a contact between the inner surface and the outer surface and a controller in communication with the plurality of sensors, wherein the controller is configured to determine a position of the first housing relative to the second housing.

Monitoring the position of an electrical connector

A vehicle includes a system that performs a method for aligning a first camera and a second camera to a reference frame of the vehicle. The first camera has a first coordinate system, and the second camera has a second coordinate system. The first camera obtains a first image. The processor is configured to obtain a first camera-to-ground transformation matrix for the first camera using the first image, wherein the first camera-to-ground transformation matrix relates the first coordinate system to a ground coordinate system, obtain a camera-to-camera transformation matrix between the first coordinate system of the first camera and the second coordinate system of the second camera, calculate a constructed camera-to-ground transformation matrix for the second camera using the first camera-to-ground transformation matrix and the camera-to-camera transformation matrix, and update the second coordinate system of the second camera using the constructed camera-to-ground transformation.

Camera to vehicle alignment with multiple camera adjustment

A system includes a platform operational to move through an environment, a navigation system operational to measure platform poses of the platform in the environment, a camera operational to generate images of the environment at multiple timestamps, and a computer. The computer is operational to receive a first image from the camera, receive a second image from the camera, estimate a view direction of the first camera pose based on a first and second platform poses and alignment parameters, generate a rendered portion of the environment with a neural radiance field technique based on the second image and the alignment parameters, generate a predicted image of the environment as observed along the view direction through the rendered portion of the environment, determine differences between the first image and the predicted image, and update one or more of the plurality of alignment parameters based on the one or more differences.

Neural radiance field based camera alignment

A method is provided that includes determining whether to perform a load shed operation for a vehicle. The method further includes responsive to determining to perform the load shed operation, issuing a command to a device to perform the load shed operation. The method further includes determining whether the device performed the load shed operation. The method further includes responsive to determining that the device failed to perform the load shed operation, opening a relay associated with the device to prevent power from being delivered to the device.

Load shedding for a vehicle

A system in a vehicle includes a lidar system to transmit incident light and receive reflections from one or more objects as a point cloud of points. The system also includes processing circuitry to identify feature points among the points of the point cloud, the feature points being horizontal feature points reflected from a horizontal surface or vertical feature points reflected from a vertical surface. The processing circuitry processes the point cloud by obtaining a normal vector corresponding to each of the points of the point cloud. The normal vector includes a first component associated with a first dimension, a second component associated with a second dimension, and a third component associated with a third dimension.

Identification of planar points in lidar point cloud obtained with vehicle lidar system

A camera misalignment detection system detecting a misalignment condition of one or more cameras for a vehicle includes one or more controllers that determine a set of matched pixel pairs, determine a feature matching ratio based on the set of matched pixel pairs, and calculate an alignment angle difference of the one or more cameras. In response to determining the feature matching ratio, the essential matrix inlier ratio, and the alignment angle difference each exceed respective threshold values, the controllers add the alignment angle difference to a queue including a sequence of historical alignment angle difference values. The controllers perform statistical filtering to determine a total number of historical alignment angle difference values within the queue that are inliers and determine a misalignment condition of the one or more cameras based on the total number of historical alignment angle difference values within the queue that are inliers.

Camera misalignment detection system for a vehicle

A vehicle, electrical system of the vehicle, and a method of detecting a fault occurring in the electrical system. The electrical system includes an electronic control unit, a sensor configured to obtain a measurement of a parameter of the electronic control unit, and a processor. The processor is configured to determine a parasitic load at the electronic control unit from the measurement of the parameter, identify a type of fault occurring at the electronic control unit due to the parasitic load based on the measurement, and perform an action at the electrical system based on the type of fault.

Enhanced fault isolation and mitigation for parasitic load using smart energy center

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Oakland Township, MI, United States of America

Xinyu Du