Gm Global Technolgoy Operations, Inc.

Aptiv Technologies Limited

Aptiv Technologies Ag

James Nicholas Nickolaou

Shuqing Zeng

Ronald Melvin Taylor

Kevin Austin O'Dea

Joel Pazhayampallil

Bakhtiar Brian Litkouhi

Jeremy A Salinger

Steven G Malson

Daniel Gandhi

Henry W Baker

Fred W Huntzicker

Mohannad F Murad

Paul R Williams

Dmitriy Feldman

Mark Edward Shields

Donald K Grimm

Charles A Green

Kevin S Kidston

Adam L Wright

Stephen W Decker

Robert D Sims, Iii

Patrick J O'Leary

Michael Paul Turski

Pamela I Labuhn

Robert Dallos, Jr

Eric L Raphael

Kai Zhang

Jeremy P Gray

Michael T Chaney, Jr

William G Herbert

David A Young

Thomas W Jozwiak

David A Osinski

Suzanne M Cody-Gump

Xiaohui Wang

Eric A Steinhilper

John H Bednarchik

Bradley Hamner

Anna M Saadatjoo

Xingping Chen

Daniel J Bartz

Leonard J Leshinsky, Jr

Benjamin M Ekola

Lufeng Shi

Norman Ellis Hovis, Jr

Evan M Pitt

Yusuf Zohar Dilawar

Neha N Gupta

The techniques and systems herein enable robot management in populated areas. Specifically, a robot management system receives, from a robot system, a requested route and time for the robot system to travel. A dynamic map that includes dynamic and temporal information about an area of the requested route is then used to determine whether the requested route and time are approved, approved with a modified route or time, or disapproved. An approval message, a modified approval message, or a disapproval message is then generated and sent to the robot system. By using the described techniques, municipalities can monitor, manage, and regulate robots across various operators and locations. Doing so may prevent a high density of robots along various routes, disruptions to the services of such robots, and/or disruptions to surrounding pedestrians and vehicles.

Robot management in populated areas

This document describes techniques for localizing a vehicle to map data, for example, to enable assisted driving and automated driving within a road. A road model builder is described that uses vehicle location data to determine its position relative to map data obtained from a provider, which can be different than the location data's source. Using the vehicle position, the road model builder maintains a current lane segment for the vehicle and its relative position within the current lane segment. Based on its relative position, the road model builder determines whether a transition to another lane segment is appropriate before setting the current lane segment to be the other lane segment. The current lane segment, when provided as an input to an assisted-driving or automated-driving system, can enable safer operation of the vehicle and use fewer computing resources than other systems.

Vehicle localization to map data

A camera includes a phased metalens positioned between an objective lens and an imager of the camera. The phased metalens is configured to adjust a focus plane of an image in a field of view of the camera in response to changes in an operating temperature of the camera. The phased metalens adjusts the focus plane for multiple frequencies or wavelengths light such that all light wave-fronts exiting the phased metalens arrive at the imager at a same time.

Camera with phased metalens

This document describes techniques, apparatuses, and systems for standard-definition (SD) to high-definition (HD) navigation route determination. An example route builder receives a navigation route generated for a host vehicle. The navigation route includes a list of waypoints generated by an SD map database. The route builder matches the list of waypoints to lane geometry data maintained in an HD map database and outputs HD navigation route to a vehicle controller. The HD navigation route includes the list of waypoints, additional waypoints, and lane geometry data. The vehicle controller can then operate the host vehicle in a roadway environment along the HD navigation route. In this way, the described techniques and systems can provide HD navigation routes required by some assisted-driving and autonomous-driving systems based on SD navigation routes.

Standard-definition to high-definition navigation route determination

This document describes vehicle localization using map and vision data. For example, this document describes a localization system that obtains a map centerline point and a vision centerline point of a lane of a roadway. The localization system also obtains the position of the vehicle. The localization system can then compare the map centerline point and the vision centerline point to generate a lateral and a longitudinal correction relative to the vehicle's position. The lateral and longitudinal corrections are used to generate a corrected position. In this way, the described localization system can provide accurate vehicle localization that addresses potential drift caused by lapsed or inaccurate positioning data and allows for the operation of assisted-driving and autonomous-driving systems at higher speeds and on roadways with tighter curves.

Vehicle localization using map and vision data

Methods and systems are described that enable radar detection prioritization based on downstream feedback. As a vehicle is traveling in a travel corridor, radar detections are received by a processor and feedback related to a downstream function is received from another processor that is executing the downstream function. For the radar detections that are determined to be within the travel corridor, respective priorities are assigned based on locations of the radar detections and the feedback related to the downstream function. The radar detections with the assigned priorities are then stored in a detection buffer along with indications of their respective assigned priorities. In this way, the detection buffer may be optimized for the downstream function by filtering out certain detections and prioritizing others. Consequently, computational loads on the downstream function are reduced while ensuring that important detections are received by the downstream function early.

Radar detection prioritization based on downstream feedback

The techniques of this disclosure relate to a system for adjusting a focus of an image. A system includes a phased metalens configured to adjust a focus of an image detected by an imaging substrate of an image sensor to compensate for warpage of the imaging substrate. The phased metalens can achieve a near-diffraction-limited focusing over incoming light wavelengths using precisely defined nanoscale subwavelength resolution structures.

Phased metalens for adjusting a focus of an image

The techniques of this disclosure relate to a system for adjusting a focus of an image. A system includes a phased metalens configured to adjust a focus of an image detected by an imaging substrate of an image sensor. The phased metalens is further configured to adjust a property of light that reaches the imaging substrate based on a change in a flatness of the imaging substrate. The property of the light that reaches the imaging substrate includes a phase of all the light that reaches the imaging substrate at a same time. The phased metalens accomplishes this by achieving near-diffraction-limited focusing over the incoming light wavelengths using precisely defined nanoscale subwavelength resolution structures.

A method of verifying the health and functionality of at least one vehicle sensor is disclosed herein. The method includes positioning the vehicle within a controlled environment test facility, displaying a predetermined test pattern to the at least one vehicle sensor as the vehicle travels within the controlled environment test facility, receiving sensor data corresponding to a performance condition of the at least one sensor from the at least one vehicle sensor, transmitting the sensor data to a remote processor, and determining a functional condition of the at least one vehicle sensor based on the sensor data.

Methods and systems for integrated vehicle sensor calibration and maintenance

A method of calculating at least one dimension of a towed vehicle includes sensing an image of the towed vehicle with an image sensing device. A feature on the towed vehicle in the sensed image of the towed vehicle is identified with a controller using a dimension determination algorithm. The dimension of the towed vehicle is determined from the identified feature of the towed vehicle, with the controller using the dimension determination algorithm. The dimension of the towed vehicle may be input into a vehicle tow controller, to enable the vehicle tow controller to control the tow vehicle.

Method of calculating dimensions of a towed vehicle

A spray nozzle system includes a housing having an upper cap, a neck portion for helping to support the housing adjacent a component, and at least one fluid flow line for supplying a fluid to an interior area of the housing. A plurality of spray nozzles may be included which are housed in the housing and in communication with the at least one fluid line for distributing the fluid over a circumferential surface of the component.

Circular spray nozzle system and method for cleaning a cylindrical surface of a component

Systems are provided for mounting a sensor to a vehicle. In one embodiment, a sensor mounting system includes a mounting assembly coupled to a body of the vehicle. The mounting assembly has a first mounting plate spaced apart from a second mounting plate by at least one isolation member. The second mounting plate is coupled to the body of the vehicle. The sensor mounting system also includes a turret housing coupled to the first mounting plate so as to be movable relative to the first mounting plate. The turret housing defines at least one receptacle for a first sensing device.

Vehicle sensor mounting systems

A method for localizing a vehicle in a digital map. GPS raw measurement data is retrieved from satellites. A digital map of a region traveled by the vehicle based on the raw measurement data is retrieved from a database. The digital map includes a geographic mapping of a traveled road and registered roadside objects. The registered roadside objects are positionally identified in the digital map by earth-fixed coordinates. Roadside objects are sensed in the region traveled by the vehicle using distance data and bearing angle data. The sensed roadside objects are matched on the digital map. A vehicle position is determined on the traveled road by fusing raw measurement data and sensor measurements of the identified roadside objects. The position of the vehicle is represented as a function of linearizing raw measurement data and the sensor measurement data as derived by a Jacobian matrix and normalized measurements, respectively.

Sensor-aided vehicle positioning system

Growing community of inventors

Clarkston, MI, United States of America

James Nicholas Nickolaou