Gm Global Technology Operations LLC

Shuqing Zeng

Wei Tong

Upali Priyantha Mudalige

Shige Wang

Bakhtiar Brian Litkouhi

Xiaofeng Frank Song

Wende Zhang

James Nicholas Nickolaou

Jinsong Wang

Igal Bilik

Kevin Austin O'Dea

Jeremy A Salinger

Yasen Hu

Roman L Millett

Fan Bai

Nikolai K Moshchuk

Mohammad Naserian

Mohannad F Murad

Lawrence A Bush

Wei Zeng

Mark Edward Shields

Rouhollah Jafari

Joel Pazhayampallil

Donald K Grimm

Sanjeev Manubhai Naik

Shengbing Jiang

Scott E Parrish

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

Daniel Gandhi

Xingping Chen

Praveen Choudhury

Sourav Dey

Chandra S Namuduri

Suresh Gopalakrishnan

Xinyu Du

Mutasim Abdurrahman Salman

Daniel J Gregoire

Bo Yu

William Joseph Chundrlik, Jr

Hualin Tan

Shahar Villeval

Joseph S Colburn

Tzvi Philipp

Curtis L Hay

Dan Levi

David M Sidlosky

Wen-Chiao Lin

Prakash Mohan Peranandam

Hariharan Krishnan

Alaa M Khamis

Arun Adiththan

Sayyed Rouhollah Jafari Tafti

Osman D Altan

Ramesh Sethu

Meng Jiang

Mohamed A Layouni

Kevin Geary

Hooman Kazemi

Jiyu Zhang

Michael A Losh

Dmitriy Feldman

Richard Gordon

Brent N Bacchus

Guangyu J Zou

Jeremy P Gray

Peter John Giles

Grant L Meade

Brent T Deep

Praneeth Nelapati

Robert A Bordo

Jimmy Qi

David A Osinski

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

Shawn W Ryan

Zachariah E Tyree

Stephen N McKinnie

Priyantha Upali Mudalige

Xiang Gong

Rickie A Sprague

David W Doerr

Yuanhong Li

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

A vehicle system includes a lidar system that obtains an initial point cloud and obtains a dual density point cloud by implementing a first neural network and based on the initial point cloud. The dual density point cloud results from reducing point density of the initial point cloud outside a region of interest (ROI). Processing the dual density point cloud results in a detection result that indicates any objects in a field of view (FOV) of the lidar system. A controller obtains the detection result from the lidar system and controls an operation of the vehicle based on the detection result.

Vehicle lidar system with neural network-based dual density point cloud generator

A system for an attention-based perception includes a camera device configured to provide an image of an operating environment of a vehicle. The system further includes a computerized device monitoring the image, analyzing sensor data to identify a feature in the image as corresponding to an object in the operating environment and assign a score for the feature based upon an identification, a location, or a behavior of the object. The computerized device is further operable to define candidate regions of interest upon the image, correlate the score for the feature to the candidate regions of interest to accrue a total region score, select some of the candidate regions for analysis based upon the total region scores, and analyze the portion of the candidate regions to generate a path of travel output. The system further includes a device controlling the vehicle based upon the output.

System and method for region of interest window generation for attention based perception

A method for facilitating uses of a code for vehicle experiences includes receiving code data from the code in a sign. The sign is disposed outside a host vehicle. The method further includes establishing a connection with a service using the code data obtained from the code and in response to establishing the connection with the service, receiving service data. The service data includes information about the service. The method further includes, in response to receiving the service data, controlling the host vehicle according to the service data received after establishing the connection with the service.

Method and system for facilitating uses of codes for vehicle experiences

The present application relates to a method and apparatus for generating a graphical user interface indicative of a vehicle underbody view including a LIDAR operative to generate a depth map of an off-road surface, a camera for capturing an image of the off-road surface, a chassis sensor operative to detect an orientation of a host vehicle, a processor operative to generate an augmented image in response to the depth map, the image and the orientation, wherein the augmented image depicts an underbody view of the host vehicle and a graphic representative of a host vehicle suspension system, and a display operative to display the augmented image to a host vehicle operator. A static and dynamic model of the vehicle underbody is compared vs the 3-D terrain model to identify contact points between the underbody and terrain are highlighted.

Method and apparatus for invisible vehicle underbody view

In an exemplary embodiment, a LiDAR is provided that is configured for installation in a mobile platform. The LiDAR includes a scanner and a light-intensity receiver. The scanner includes a light source configured to direct illumination in an illuminating direction. The light-intensity receiver includes one or more light-intensity sensors; and one or more lens assemblies configured with respect to the one or more light-intensity sensors, such that that at least one sensor plane from the one or more light-intensity sensors is tilted to form a non-zero angle with at least one equivalent lens plane from the one or more lens assemblies, transferring the sensor focal plane to be align with the main light illumination direction and be consistent with the direction of movement of a mobile platform.

LiDAR vision systems

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Sterling Heights, MI, United States of America

Shuqing Zeng