Gm Global Technolgoy Operations, Inc.

Steering Solutions IP Holding Corporation

Bo Yu

Fan Bai

Donald K Grimm

Vivek Vijaya Kumar

Joon Hwang

Gui Chen

Hariharan Krishnan

Shu Chen

Carl P Darukhanavala

Shuqing Zeng

David P Pop

Robert A Hrabak

Jinzhu Chen

Kamran Ali

Brent Navin Roger Bacchus

Mason D Gemar

Ian Y Hwa

Ashok Yendluri

Lakshmi V Thanayankizil

Wen-Chiao Lin

Wenyuan Qi

David E Bojanowski

Ibrahim A Badiru

Raed N Abuaita

Jiabin Shen

Yuan Yuan Zhang

Markus Jochim

Scott R Kloess

Massimo Osella

Robert G Izak

John T Zuzelski

Robert A Bordo

Larry G Gepfrey

Yu Cao

Jarred James Versical

Adam J Kloess

Carolyn Heather MacLeod

Xinyu Du

Yilu Zhang

Mutasim Abdurrahman Salman

Hualin Tan

SeyedAlireza Kasaiezadeh Mahabadi

Anthony J Champagne

Tejas M Varunjikar

Curtis L Hay

Timothy Wesley Kaufmann

John J Correia

Soheil Samii

Pulak Bandyopadhyay

Brian K Saylor

Mohamed A Layouni

Lawrence A Bush

Michael A Losh

Ruixing Long

Qi Zhang

Christopher J Mielke

Xiaofeng Mao

Yubiao Zhang

David A Craig

Scott P Sherman

Nojan Medinei

Bharath Pattipati

Rakesh Kumar

Prachi Joshi

Jimmy Qi

Sandeep Vig

Kevin K Hoang

Yuchen Zhou

Alexander M Allan

Tianxiang Cao

Adam Douglas Stanton

Ramesh Ramiah

Quan Zhang

Can Carlak

Pei Xu

Jonathan Wilson

Donald G Wirkner

Christopher C Au

Jonathan Howard Johnston

Madhu Max Seenisamy

Muhammad Rehan

Heng Zhou

Coburn C Bland, Iii

Nicholas V Katcherian

Guanyang Luo

Yuan Zhang

A system for augmented-reality (AR)-assisted vehicle parking with operator-in-loop selections and alignments of feature point vicinities (FPVs) includes a vehicle, human-machine interfaces (HMIs), sensors detecting FPVs of a parking location and controllers. The controllers have a processor, memory, and input/output (I/O) ports in communication with the HMIs and sensors. The memory stores an AR-assisted parking application (ARAPA) executed by the processor. The AR-assisted parking application (ARAPA) has a training session (TS) and a live session (LS), and is activated upon completing parking at a parking spot at the parking location. The TS portion trains the ARAPA to recognize FPVs of the parking spot. The LS portion prompts an operator to select the parking spot, guides the operator into the parking spot by displaying trained FPVs onto live sensor data displayed on the HMI, and instructs the operator to align the trained FPVs with corresponding real-world FPVs by maneuvering the vehicle.

Augmented reality assisted parking with driver-in-loop selections and alignments of feature point vicinities

A method for crowd-sourcing lane line map data for a vehicle includes receiving a plurality of observations. The method also includes classifying the plurality of observations into a plurality of observation categories. Each of the plurality of observation categories includes at least one of the plurality of observations. The method also includes determining a plurality of aligned point clouds based at least in part on the plurality of observations. One of the plurality of aligned point clouds corresponds to each of the plurality of observation categories. The method also includes determining a plurality of lane line maps based at least in part on the plurality of aligned point clouds. One of the plurality of lane line maps corresponds to each of the plurality of aligned point clouds. The method also includes updating a map database based at least in part on the plurality of lane line maps.

Crowd-sourcing lane line maps for a vehicle

A system for fusing two or more versions of map data together includes one or more central computers that receive road network data representing a road network for a predefined geofenced area. The central computers compute a plurality of points that are each positioned at a predetermined distance from one another. The central computers create a plurality of bounding boxes for the road network based on the plurality of points and create a set of closest matched map data points for each bounding box that is part of the road network by executing a map-matching registration algorithm to align the two or more versions of map data with one another. The central computers execute a maximum likelihood estimation algorithm to determine probability distribution parameters of the set of closest matched map data points compared to the ground truth map data.

System for fusing two or more versions of map data

A method for crowd-sourcing lane line map data for a vehicle may include receiving a plurality of observations. The plurality of observations includes at least a first observation and a second observation. The method further may include generating a point cloud alignment vector based at least in part on the plurality of observations. The method further may include generating an optimized aligned point cloud based at least in part on the point cloud alignment vector. The method further may include determining a lane line map based at least in part on the optimized aligned point cloud. The method further may include updating a map database based at least in part on the lane line map.

A method for merging lane line maps includes determining a road topology of a road segment. The method also includes identifying a plurality of fused points based at least in part on the road topology and based at least in part on a first lane line map of the road segment and a second lane line map of the road segment. The method also includes forming a fused lane line map based at least in part on the plurality of fused points. The method also includes performing a first action based at least in part on the fused lane line map.

Merging lane line maps for a vehicle

Techniques are provided for vehicle oscillation control. In one embodiment, the techniques involve identifying an oscillation at a steering wheel or steering column of a vehicle, upon determining that the oscillation exceeds an oscillation of interest threshold, identifying the oscillation as an oscillation of interest, classifying a cause of the oscillation of interest, and mitigating the oscillation of interest based on the classification of the cause.

Vehicle oscillation control

A vehicle system for determining a road wheel angle of a vehicle includes sensors configured to detect vehicle parameters including at least a steering rack load on a steering rack of the vehicle, and a control module in communication with the sensors. The control module is configured to calculate a kingpin torque based on the sensed steering rack load and a wheel-to-body displacement of the vehicle, determine a roll steer angle based on the wheel-to-body displacement of the vehicle, determine a road wheel angle of the vehicle based on the roll steer angle and the kingpin torque, and generate a signal indicative of the road wheel angle to control at least one vehicle control system. Other example vehicle systems and methods for determining a road wheel angle of a vehicle are also disclosed.

Vehicle systems and methods for estimating road wheel angles

A method and system of map data reconciliation in connected vehicles. The method includes processing information to detect a same object; determining an identification of the same object; determining a confidence level of the identification of the same object; determining a probability mass function (PMF) of the identification of the same object using Dempster-Shafer Theory; determining a most probably identification of the same object based on the PMF; and communicating the most probably identification of the same object to a vehicle to implement a vehicle function. The system includes a conflict resolution module configured to receive confidence levels from multiple vehicles detecting the same object and to determine a reconciled identification of the same object using an algorithm based on Dempster-Shafer Theory, and a weight calculation module configured to apply a weight function based on a scene context weight function and on a historical score of the vehicles.

Method and system of inconsistent map data reconciliation in connected vehicles

A method for quantifying map errors includes receiving first map data and second map data. The method includes receiving a road topographic map. The method further includes dividing the road into road segments. The method further includes creating a plurality of bounding boxes for each of the plurality of road segments. The method includes creating a first map tile and a second map tile by filtering out the bounding boxes. The method includes executing point cloud registration to align the plurality of first data points in the first map tile with the plurality of second data points in the second map tile to determine a plurality of absolute offsets between the plurality of first data points and the plurality of second data points. The method includes determining a relative map error between the first map and the second map based on the absolute offsets.

Method and system for quantifying map errors

A system and method for locating a trailer coupler on a trailer to assist with the coupling of the trailer coupler to a vehicle hitch on a vehicle. The system includes a human-machine interface, a camera, a global positioning system, a remote computing system, a vehicle-to-infrastructure communication network, controllers, a memory, sensors, and a trailer coupler labeling application. The trailer coupler labeling application includes: sensing at least one of a hitched state and an unhitched state of the trailer coupler, capturing an optical data with the camera when the vehicle hitch is in the hitched and unhitched state, determining a distance of the trailer coupler relative to the camera when the vehicle hitch is in the hitched state, identifying the location of the trailer coupler on an image defined by the captured data and creating a label on the image of the trailer coupler.

System and method to locate and label a trailer coupler

A method can be used to provide smart notifications to avoid collisions while the vehicle maneuvers in a tight structural environment, such as a home garage or an underground parking lot. The method includes receiving historical vehicle-trajectory data. The historical vehicle-trajectory data includes the location and the heading of the vehicle for each of the plurality of historical trajectories along the structure. The method further includes clustering the plurality of historical trajectories of the vehicle along the structure by types of maneuvers to generate a plurality of trajectory clusters. The method also includes creating a probability distribution bitmap using the plurality of trajectory clusters and creating a topographic map based on the probability distribution bitmap.

Smart notifications for vehicle parking garage maneuvers

A global positioning system (GPS)-bias detection and reduction system including a GPS-bias model having GPS statistical data creating a database representing data collected from a vehicle group having thousands or multiple thousands of vehicles saved in a database. At least one newly collected vehicle GPS data point is compared to the GPS statistical data to reduce negative effects of GPS-bias and to update the vehicle GPS-bias correction based on a previous GPS-bias model. A selected road node and a segment of a roadway have a map matching performed using a nearest service from a collection location of the GPS statistical data. A GPS-bias is calculated using a look-up of the database. An estimated horizontal position error (EHPE) defining a quality indicator is applied to distinguish a good quality GPS statistical data from a poor quality GPS statistical data.

Global positioning system bias detection and reduction

A method of determining the position of a vehicle includes generating a vehicle-based point cloud of objects in proximity to the vehicle, referenced to a vehicle-based coordinate system. The method also includes receiving an infrastructure-based point cloud, referenced to a global coordinate system, of objects detected by a camera mounted at a fixed location external to the vehicle, and registering the vehicle-based point cloud with the infrastructure-based point cloud to determine the vehicle position in the global coordinate system.

Method and apparatus for leveraging infrastructure camera for vehicle positioning

An automobile vehicle visual wireless-based positioning system, incudes an automobile vehicle having a radio receiver. A map contains candidate locations of access-points (APs) and access-point corresponding media-access-control (MAC) IDs. A wireless range sensor determines different ranges of various detected APs visible to the automobile vehicle. An image collection feature identifies image data visible to the automobile vehicle. A real-time feature matching element matching features identified by image collection feature with data from the map. A filter receives an output from the real-time feature matching element to generate an automobile vehicle pose.

Visual wireless based positioning exploiting multipath for outdoor positioning

A vehicle system is provided for correcting in real-time a camera-based estimated position of a road object. The system includes a camera for generating an image input signal including image sensor data associated with the road object. The system further includes one or more input devices for generating a vehicle input signal including vehicle sensor data associated with a position, a speed, and a heading of the vehicle. The system further includes a computer, which includes one or more processors and a non-transitory computer readable medium (CRM) storing instructions. The processor is programmed to match the image sensor data and the vehicle sensor data to one another based on a common time of collection. The processor is further programmed to determine an error model and a deviation of a current camera-based position from a predicted position. The processor is further programmed to update the error model based on the deviation.

System and method for correcting in real-time an estimated position of a road object

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Troy, MI, United States of America

Bo Yu