Gm Global Technology Operations LLC

University of Southern California

Kamran Ali

Fan Bai

Donald K Grimm

Bo Yu

Wende Zhang

Mohammad Naserian

Lakshmi V Thanayankizil

Vivek Vijaya Kumar

Hariharan Krishnan

Bhargavi Chandrakumar

Andreas F Molisch

Curtis L Hay

Jinzhu Chen

Sayyed Rouhollah Jafari Tafti

Mohamed A Layouni

Michael A Losh

Constandi J Shami

Prachi Joshi

Ramesh Ramiah

Jiabin Shen

Pei Xu

Anna M Lusk

Madhu Max Seenisamy

Maria Del Mar Davila

Nikhil Kowshika Nataraja

Sudhanshu Sharma

Kenneth Weber

Guanyang Luo

Pasit Asavisanu

A preemptive maneuvering system for viewing a status of a traffic control device by one or more cameras of an autonomous vehicle includes one or more controllers, where a moving obstruction is in front of the autonomous vehicle. The one or more controllers execute instructions to instruct the autonomous vehicle to execute one or more preemptive maneuvers to position the autonomous vehicle at the target following distance from the moving obstruction along the roadway.

Preemptive maneuvering system for viewing a traffic control device

A system for range sensor-based calibration of roadside cameras includes one or more cameras and range sensors with overlapping fields of view of a roadway. Control logic stored within and executed by a controller includes control logic for capturing image and range sensor data, for filtering the data to focus on first and second regions of interest (ROIs); for filtering the data to focus on first and second movements of interest (MOIs); for filtering the data to focus on first and second objects of interest (OOIs); for filtering the data to focus on first and second positions of interest (POIs); and for defining that objects detected by the camera and sensor and that satisfy both the first and second ROI, MOI, OOI, and POI filters are matching objects. The control logic calibrates the camera by applying a correction factor based on the matching objects.

Range sensor based calibration of roadside cameras in ADAS applications

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

Systems and methods of harvesting wireless energy for climate control in a cabin of a vehicle are provided. The method comprises receiving an electromagnetic (EM) signal of EM radiation having EM energy to define an EM current. The method further comprises filtering the EM energy to regulate the EM current converting the EM current to direct current. The method further comprises storing the direct current for powering a battery-free wireless sensing unit and powering the battery-free wireless sensing unit with the direct current. After powering the battery-free wireless sensing unit, the method comprises sensing an actual condition in the cabin, the actual condition being one of actual temperature and actual humidity in the cabin. Furthermore, the method comprises adjusting one of temperature and humidity in the cabin in response to a difference between the actual condition and a set condition.

System and method of harvesting wireless energy for climate control in a vehicle

A system for activating a voice assistant of a vehicle includes a microphone, a first wireless module, a second wireless module, and a controller in electrical communication with the microphone, the first wireless module, and the second wireless module. The controller is programmed to transmit a plurality of original training signals on a plurality of subcarriers using the first wireless module. The controller is further programmed to receive a plurality of propagated training signals using the second wireless module. The controller is further programmed to determine a deviation between the plurality of original training signals and the plurality of propagated training signals. The controller is further programmed to identify a motion marker based at least in part on the deviation. The controller is further programmed to activate the voice assistant of the vehicle to receive a voice command using the microphone based at least in part on the motion marker.

System and method for activating a voice assistant for a vehicle

A system for video recording for a vehicle includes a camera system, a first wireless module, a second wireless module, and a controller in electrical communication with the camera system, the first wireless module, and the second wireless module. The controller is programmed to transmit a plurality of original training signals on a plurality of subcarriers using the first wireless module. The controller is further programmed to receive a plurality of propagated training signals using the second wireless module. The controller is further programmed to determine a deviation between the plurality of original training signals and the plurality of propagated training signals. The controller is further programmed to determine an occupancy status of an environment surrounding the vehicle based at least in part on the deviation and record a video using the camera system in response to determining that the occupancy status is an occupied status.

System and method for triggering video recording for a vehicle

An ego vehicle includes a system having one or more input devices for generating an input signal associated with a remote vehicle. The system further includes a computer having one or more processors programmed to receive the input signal from the input device. The processor is further programmed to determine a speed profile of the remote vehicle based on the input signal, determine that the remote vehicle is a traffic impediment based on the input signal, and determine a predicted slowdown event associated with the traffic impediment and the speed profile. The processor is further programmed to generate an actuation signal, in response to the processor determining the speed profile, the traffic impediment, and the predicted slowdown event. The notification device provides the notification of the predicted slowdown event to a user in response to the notification device receiving the actuation signal from the processor.

Ego vehicle having a system for passing a remote vehicle associated with a predicted slowndown event

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 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

A system to locate seat controls and belt buckles includes a vehicle having at least one seat. A seat belt system is provided with the at least one seat having a seat belt, a belt latch and a seat belt buckle. A haptic device is provided with the seat belt system, the haptic device generating a haptic signal.

Haptic cues to help locate seat controls and belt buckles

An occupant detection system for determining a unique position of a detected presence within an interior cabin area of a vehicle includes a plurality of signal conversion devices each including conversion circuitry. Each of the plurality of signal conversion devices are assigned to a specific position within the interior cabin area of the vehicle. The conversion circuitry for the plurality of signal conversion devices include unique sub-carrier frequency that corresponds to the unique position within the interior cabin area of the vehicle. The occupant detection system includes a wireless control module including a multi-band transceiver having a first transceiver configured to transmit and receive signals on a first frequency band and a second transceiver configured to transmit and receive signals on a second frequency band.

Occupant detection system for determining position in a vehicle based on coarse-grained localization

A scan matching and radar pose estimator for determining a final radar pose for an autonomous vehicle includes an automated driving controller that is instructed to determine a hyper-local submap based on a predefined number of consecutive aggregated filtered data point cloud scans and associated pose estimates. The automated driving controller determines an initial estimated pose by aligning a latest aggregated filtered data point cloud scan with the most recent hyper-local submap based on an iterative closest point (ICP) alignment algorithm. The automated driving controller determines a pose graph based on the most recent hyper-local submap and neighboring radar point cloud scans, and executes a multi-view non-linear ICP algorithm to adjust initial estimated poses corresponding to the neighboring radar point cloud scans in a moving window fashion to determine a locally adjusted pose.

Scan matching and radar pose estimator for an autonomous vehicle based on hyper-local submaps

A scan aggregator and filter for an autonomous vehicle includes a plurality of radar sensors, where each radar sensor performs a plurality of individual scans of a surrounding environment to obtain data in the form of a radar point cloud including a plurality of detection points. The scan aggregator and filter also includes an automated driving controller in electronic communication with the plurality of radar sensors. The automated driving controller is instructed to filter each of the individual scans to define a spatial region of interest and to remove the detection points of the radar point cloud that represent moving objects based on a first outlier-robust model estimation algorithm. The automated driving controller aggregates a predefined number of individual scans together based on a motion compensated aggregation technique to create an aggregated data scan and applies a plurality of density-based clustering algorithms to filter the aggregated data scan.

Filtering and aggregating detection points of a radar point cloud for an autonomous vehicle

A calibration pipeline for 6DoF alignment parameters for an autonomous vehicle includes an automated driving controller instructed to receive inertial measurement unit (IMU) poses and final radar poses and determine smoothened IMU poses from the IMU poses and smoothened final radar poses from the final radar poses. The automated driving controller aligns the smoothened IMU poses and the smoothened final radar poses with one another to create a plurality of radar-IMU A, B relative pose pairs. The automated riving controller determines a solution yielding a threshold number of inliers of further filtered radar-IMU A, B relative pose pairs, randomly samples the further filtered radar-IMU A, B relative pose pairs with replacements several times to determine a stream of filtered radar-IMU A, B relative pose pairs, and solves for a solution X for the stream of filtered radar-IMU A, B relative pose pairs.

Calibration pipeline for estimating six degrees of freedom (6DoF) alignment parameters for an autonomous vehicle

An occupant detection system for an interior cabin area of a vehicle includes one or more signal conversion devices including conversion circuitry and a wireless control module including a multi-band transceiver having two or more transceivers. The wireless control module executes instructions to continuously send, by a first transceiver, signals on a first frequency band. The wireless control module executes instructions to receive, by a second transceiver, signals on a second frequency band from a signal conversion device. The wireless control module compares original training symbols from the signals on the first frequency band with training symbols from the signals on the second frequency band to determine a change in value of in one or more channel state information (CSI) parameters, and determines one or more occupants are present within the interior cabin area of the vehicle based on the change in one or more CSI parameters.

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Troy, MI, United States of America

Kamran Ali