Gm Global Technolgoy Operations, Inc.

Shuqing Zeng

Wei Tong

Upali Priyantha Mudalige

Shige Wang

Bakhtiar Brian Litkouhi

Xiaofeng Frank Song

Wende Zhang

Fan Bai

James Nicholas Nickolaou

Jinsong Wang

Igal Bilik

Mohammad Naserian

Bo Yu

Kevin Austin O'Dea

Jeremy A Salinger

Yasen Hu

Roman L Millett

Nikolai K Moshchuk

Mohannad F Murad

Wei Zeng

Lawrence A Bush

Joon Hwang

Mark Edward Shields

Gui Chen

Rouhollah Jafari

Joel Pazhayampallil

Donald K Grimm

Sanjeev Manubhai Naik

Shengbing Jiang

Scott E Parrish

Mason D Gemar

Jordan B Chipka

Yilu Zhang

Allan K Lewis

Qingrong Zhao

David R Petrucci

Gregg R Kittinger

Varsha K Sadekar

Stephen W Decker

Yubiao Zhang

Mutasim A Salman

Jay H Sim

Carl P Darukhanavala

Daniel Gandhi

Xingping Chen

Praveen Choudhury

Sourav Dey

Chandra S Namuduri

Suresh Gopalakrishnan

Xinyu Du

Daniel J Gregoire

Mutasim Abdurrahman Salman

William Joseph Chundrlik, Jr

Hualin Tan

Shahar Villeval

Tzvi Philipp

Joseph S Colburn

Curtis L Hay

Vivek Vijaya Kumar

Dan Levi

Wen-Chiao Lin

David M Sidlosky

Prakash Mohan Peranandam

Alaa M Khamis

Arun Adiththan

Hariharan Krishnan

Sayyed Rouhollah Jafari Tafti

Mohamed A Layouni

Osman D Altan

Ramesh Sethu

Meng Jiang

Kevin Geary

Hooman Kazemi

Michael A Losh

Jiyu Zhang

Dmitriy Feldman

Richard Gordon

Jeremy P Gray

Guangyu J Zou

Brent N Bacchus

Grant L Meade

Peter John Giles

Brent T Deep

Robert A Bordo

Praneeth Nelapati

David A Osinski

Jimmy Qi

Thanura Ranmal Elvitigala

Xian Zhang

Farui Peng

Stephen G Lusko

Bradley Hamner

Brian W Mahnken

Jiang-Ling Du

Manpreet S Bajwa

Hansi Liu

Jeffrey Louis Brown

Can Carlak

Shawn W Ryan

Ruiyang Zhu

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 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 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

A dynamic side blind zone method includes determining that a host vehicle is approaching a first lane that is nonparallel to a second lane. The host vehicle is moving in the second lane. The method further includes activating an adaptive side blind zone alert system of the host vehicle in response to determining that the host vehicle is approaching the first lane that is nonparallel to the second lane, determining a warning zone in response to activating the adaptive side blind zone alert system of the host vehicle, and detecting a remote vehicle inside the warning zone after determining the warning zone. The remote vehicle is moving in the first lane. The method further includes providing an alert to a vehicle user of the host vehicle in response to detecting that the remote vehicle is inside the warning zone.

Adaptive side blind zone alert system and method

A perception processing system includes a memory and a main controller. The main controller includes modules and implements a data processing pipeline including algorithm stages, which are executed in parallel relative to sets of data and are executed sequentially relative to each of the sets of data. The algorithm stages share resources of the modules and the memory to process the sets of data and generate perception information. One of the modules executes global and local controllers. The global controller sets a processing rate for the local controllers. The local controllers monitor current processing rates of the algorithm stages. When one of the current processing rates is less than the set processing rate, the corresponding one of the local controllers sends a first signal to the global controller and in response the global controller sends a broadcast signal to the local controllers to adjust the current processing rates.

Perception processing with multi-level adaptive data processing flow rate control

A radar signal processing method for improved occupant vital monitoring includes a method for detecting vital signs using at least one radar sensor mounted to a vehicle. The method includes detecting a plurality of target candidates using the at least one radar sensor, analyzing the plurality of target candidates to generate a list of targets, steering the at least one radar sensor towards the target using the angle of arrival to the target, and detecting a vital signal of the target based on the range using the at least one radar sensor.

Radar signal processing method for improved occupant vital monitoring

A method includes receiving sensed vehicle-state data, actuation-command data, and surface-coefficient data from a plurality of remote vehicles, inputting the sensed vehicle-state data, the actuation-command data, and the surface-coefficient data into a self-supervised recurrent neural network (RNN) to predict vehicle states of a host vehicle in a plurality of driving scenarios, and commanding the host vehicle to move autonomously according to a trajectory determined using the vehicle states predicted using the self-supervised RNN.

Unified self-supervisory learnable vehicle motion control policy

Machine learning-based predictive dynamics for vehicle motion control

A method for validating an autonomous vehicle performance using nearby traffic patterns includes receiving remote vehicle data. The remote vehicle data includes at least one remote-vehicle motion parameter about a movement of a plurality of remote vehicles during a predetermined time interval. The method further includes determining a traffic pattern of the plurality of remote vehicles using the at least one remote-vehicle motion parameter. The method includes determining a similarity between the traffic pattern of the plurality of remote vehicles and movements of the host vehicle. Further, the method includes determining whether the similarity between the traffic pattern of the plurality of remote vehicles and movements of the host vehicle is less than a predetermined threshold. Also, the method includes commanding the host vehicle to adjust the movements thereof to match the traffic pattern of the plurality of remote vehicles.

Method and system for validating autonomous vehicle performance using nearby traffic patterns

A perception system is adapted to receive visual data from a camera and includes a controller having a processor and tangible, non-transitory memory on which instructions are recorded. A subsampling module, an object detection module and an attention module are each selectively executable by the controller. The controller is configured to sample an input image from the visual data to generate a rescaled whole image frame, via the subsampling module. The controller is configured to extract feature data from the rescaled whole image frame, via the object detection module. A region of interest in the rescaled whole image frame is identified, based on an output of the attention module. The controller is configured to generate a first image based on the rescaled whole image frame and a second image based on the region of interest, the second image having a higher resolution than the first image.

Perception system with attention module for processing visual data

Presented are embedded control systems with logic for computation and data sharing, methods for making/using such systems, and vehicles with distributed sensors and embedded processing hardware for provisioning automated driving functionality. A method for operating embedded controllers connected with distributed sensors includes receiving a first data stream from a first sensor via a first embedded controller, and storing the first data stream with a first timestamp and data lifespan via a shared data buffer in a memory device. A second data stream is received from a second sensor via a second embedded controller. A timing impact of the second data stream is calculated based on the corresponding timestamp and data lifespan. Upon determining that the timing impact does not violate a timing constraint, the first data stream is purged from memory and the second data stream is stored with a second timestamp and data lifespan in the memory device.

Intelligent vehicles with distributed sensor architectures and embedded processing with computation and data sharing

A virtual LiDAR sensor system fora motor vehicle includes a plurality of camera modules and algorithms that generate a depth image, a RGB image, and a segmentation information image. The system is implemented with an algorithm that associates the backscattered signals with information from a color-reflectivity table, incident angle determination and depth information.

Real-time virtual LiDAR sensor

An automobile vehicle continuous validation system includes a backend collecting data from a vehicle fleet and wirelessly communicating with the vehicle fleet. The backend is in wireless communication with at least one client. A vehicle module is provided on-board individual ones of multiple automobile vehicles of the vehicle fleet and performing an on-board vehicle validation analysis. A fleet-based validation module provided either at the backend or cloud based manages data defining a configuration of and a capability of the multiple automobile vehicles of the vehicle fleet. A validation manager generates validation tasks based on a user's definition or a desired production of the validation tasks of the validation analysis and a fleet vehicle availability. A client-side module remote from the multiple automobile vehicles of the vehicle fleet has interface items applied by the at least one client seeking to perform the validation analysis.

System and method enabling validation-as-a-service for continuous validation of advanced driver assistance systems and autonomous vehicles

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Sterling Heights, MI, United States of America

Shuqing Zeng