Gm Global Technology Operations LLC

Carnegie Mellon University

University of Southern California

University of Michigan

Rutgers, the State University of New Jersey

Fan Bai

Donald K Grimm

Bo Yu

Jinzhu Chen

Lakshmi V Thanayankizil

David P Pop

Robert A Hrabak

Vivek Vijaya Kumar

John Sergakis

Dan Shan

Wende Zhang

Mohammad Naserian

Kamran Ali

David E Bojanowski

Shu Chen

Shuqing Zeng

Chuan Li

Massimo Osella

Joon Hwang

Xi Ju

Leonard Nieman

Paul Edward Krajewski

Soheil Samii

Jace C Stokes

Mohannad F Murad

Hariharan Krishnan

Esther Anderson

Gui Chen

Carl P Darukhanavala

Ashok Yendluri

Timothy J Talty

Upali Priyantha Mudalige

Omer Tsimhoni

John J Correia

Richard Gordon

Aaron Adler

Mason D Gemar

Jiang L Du

Ramesh Govindan

Xinyu Du

Yilu Zhang

Joseph K Moore

Marco Gruteser

Douglas C Martin

Xin Yu

Craig A Kollar

Joseph G Machak

Lawrence A Bush

Vijayakumar Bhagavatula

Markus Jochim

Cem U Saraydar

Chia-Hsiang Liu

David A Craig

Carlos E Arreaza

Amin Abdossalami

Mark T Gaylord

Bhargavi Chandrakumar

Zheng Li

Bakhtiar Brian Litkouhi

Andreas F Molisch

Youssef A Ghoneim

Norman Joseph Weigert

Mutasim Abdurrahman Salman

Wei Tong

Zhuoqing Morley Mao

Jenne-tai Wang

Xidong Tang

Mark N Howell

Pulak Bandyopadhyay

Satnam Singh

Akram M Abdel-Rahman

Sayyed Rouhollah Jafari Tafti

Thomas E Utter

Mohamed A Layouni

Gavin D Holland

Chaminda Basnayake

Michael A Losh

Bing Deng

Thomas E Fuhrman

Daniel D Stancil

Ozan K Tonguz

Eric L Raphael

Wen Gu

Xiaofeng Mao

Guangyu J Zou

Halit Zengin

Unmesh Dutta Bordoloi

Jeremy Bos

Robert A Bordo

Travis S Hester

Karen Juzswik

Anthony Gerard Lobaza

Prachi Joshi

Jimmy Qi

Sandeep Vig

Kevin K Hoang

Michael J Quinn

Hannah Elizabeth Wagner

Yuchen Zhou

Bhaskar Krishnamachari

A collaborative perception system that creates a cooperative perception map based on perception data collected by a plurality of vehicles and includes one or more central computers in wireless communication with one or more controllers of each of the plurality of vehicles located in an environment containing a plurality of static roadside objects. The one or more central computers executes instructions to rank each static roadside object in the environment based on a respective utility function value and create the cooperative perception map by annotating map data of the environment based on a respective rank and geographic location of each of the static roadside objects located in the environment.

Collaborative perception system for creating a cooperative perception map

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 maintaining network quality of service (QoS) for in-vehicle applications includes receiving historical network speed data from a plurality of remote actors and mapping a network speed along the road ahead of the host vehicle using the historical network speed data from the plurality of remote actors and identifying a bad network coverage zone along the road ahead of the host vehicle. The method further includes determining an amount of time that the host vehicle will spend in the bad network coverage zone, determining an amount of application data to cache as a function of the amount of time that the host vehicle will spend in the bad network coverage zone, and caching the amount of application data before the host vehicle reaches the bad network coverage zone.

Methods to maintaining network quality of service for in-vehicle applications

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

A system and method of identifying and quantifying congestion within a traffic stream including obtaining telemetric data from a plurality of vehicles traveling within a plurality of normalized road segments, determining a property of each normalized road segment based on the telemetric data and a road profile for each road segment, determining a disruption score indicative of a level of disruption in a traffic flow within each road segment, mapping the road segments within a two-dimensional spatial-temporal grid of cells, wherein each cell represents a normalized road segment at a specified time, for each cell, determining if the traffic flow is congested, identifying a congested traffic stream including a plurality of contiguous cells that have congested traffic flow, quantifying the congested traffic stream, and providing an output signal.

Identifying and quantifying congestion within a traffic stream

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 multi-static radar detection system for a vehicle includes one or more receivers for collecting multi-carrier modulation signals emitted by one or more transmitters that are positioned at base stations located in an environment surrounding the vehicle. The multi-carrier modulation signals include one or more types of reference signals. The multi-static radar detection system also includes one or more controllers in electronic communication with the one or more receivers. The one or more controllers execute instructions to collect, by the one or more receivers, a multi-carrier modulation signal emitted by the one or more transmitters. The one or more controllers determine a Doppler shift and a time delay of a respective object located within the environment surrounding the vehicle based on an interpolated time-frequency grid; and transform a value for the Doppler shift into a velocity value and a value for the time delay into a range value.

Multi-static radar detection system for a vehicle

A system for optimizing traffic signal timing using telemetry includes: wirelessly connected devices (WCDs), traffic operations centers (TOCs) controlling one or more traffic signals, and control modules storing and continuously executing control logic in a closed loop. The control logic continuously receives real-time WCD telemetry data and performs real-time processing of the telemetry data to match the telemetry data to physical locations. Processed telemetry data is aggregated to determine WCD trajectories. The system selectively performs time-of-day (TOD), offset, and green split optimizations, and receives the WCD trajectories and outputs of the TOD, offset, and green split optimization within an API. The system generates an optimized signal plan from data from the API, and verifies the optimized signal plan. The optimized signal plan is received by the TOCs and selectively uploaded to the traffic signals. The optimized signal plan increases throughput of traffic by decreasing total delays, and quantities of stops.

Optimizing traffic signal timing using vehicle telemetry data

A method for video streaming includes receiving a video service request from a vehicle. The video service request is indicative of a request for a video content. The method further includes receiving a bandwidth prediction of a cellular network from the vehicle, and a list of available video codec configurations for a video content associated with the video service request. Moreover, method includes determining a selected video codec configuration based on the bandwidth prediction of the vehicle, determining that the selected video codec configuration is part of the list of available video codec configurations, and providing the video content to the vehicle using the selected video codec configuration.

Method and system for video streaming with cellular data rate prediction

Systems and methods of diagnosing sensor performance of an ultra wide band (UWB) sensor localization for a vehicle are provided. The method comprises receiving sensor signals from at least four UWB anchors and a UWB tag for a time period. The sensor signals represent anchor coordinates and real-time distances between the tag and each anchor. The method comprises aligning the sensor signals to define aligned data. The method comprises calculating a predicted location of the UWB tag based on the aligned data and a least square of error to define a first constructed matrix and a second constructed matrix. The method comprises determining a local error of each of the at least four UWB anchors based on the least square of error and comparing each local error with an error threshold to define a first threshold high. The method comprises determining an erratic anchor based on the first threshold high.

System and method for diagnosing sensor performance of an ultra wide band sensor localization

A system for processing a plurality of video recordings for a vehicle includes a camera system including a camera buffer. The system also includes a video storage device. The system also includes a vehicle communication system configured to communicate with a remote server. The system also includes a vehicle controller. The vehicle controller is programmed to capture one of the plurality of video recordings using the camera system. The one of the plurality of video recordings is stored to the camera buffer. The vehicle controller is also programmed to generate at least one event video file based on the one of the plurality of video recordings and save the at least one event video file to the video storage device. The vehicle controller is also programmed to upload the at least one event video file from the video storage device to the remote server using the vehicle communication system.

Vehicle video recording framework

Systems and methods for diagnosing sensor performance of a UWB sensor localization are provided. The system comprises a UWB tag, at least four UWB anchors, and a gateway. The gateway comprises an ECU arranged to receive sensor signals from the UWB anchors. The ECU comprises a preprocessing module arranged to align the sensor signals defining aligned data and is arranged to determine intersections of the aligned data defining points of intersections. The system further comprises a clustering module arranged to cluster the points of intersections defining at least one cluster of points of the UWB anchors to calculate a clustering quality and a clustering variance of each of the at least one cluster. The ECU is arranged to find a clustering contribution of each anchor defining a first contribution low of one of the anchors and is arranged to determine an erratic anchor based the first contribution low.

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

A method of creating a high-definition (HD) map of a roadway includes receiving a multi-layer probability density bitmap. The multi-layer probability density bitmap represents a plurality of lane lines of the roadway sensed by a plurality of sensors of a plurality of vehicles. The multi-layer probability density bitmap includes a plurality of points. The method further includes recursively conducting a hill climbing search using the multi-layer probability density bitmap to create a plurality of lines. In addition, the method includes creating the HD map of the roadway using the plurality of lines determined by the hill climbing search.

Hill climbing algorithm for constructing a lane line map

A method for enabling vehicle connected services for a hearing-impaired vehicle occupant includes receiving sensor data from a plurality of sensors of a vehicle. The sensor data includes scene data indicative of a scene inside the vehicle and outside the vehicle. The method further includes determining that the hearing-impaired vehicle occupant is articulating sign language using the sensor data. Also, the method includes determining a vehicle-occupant message corresponding to the sign language articulated by the hearing-impaired vehicle occupant to generate vehicle occupant-message data in response to determining that the hearing-impaired vehicle occupant is articulating sign language. The method further includes transmitting the vehicle occupant-message data and the scene data to a remote system in response to generating the vehicle occupant-message data. Further, the method includes receiving a vehicle connected service from the remote system.

Method and system for enabling vehicle connected services for hearing-impaired vehicle occupants

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Ann Arbor, MI, United States of America

Fan Bai