Ford Global Technolgoies, LLC

Ford Motor Company Limited

Lear Corporation

Jp Morgan Chase Bank

Jeffrey Thomas Remillard

Willes H Weber

Timothy Fohl

Michael Anthony Marinelli

Kenneth Edward Nietering

Helen Elizabeth Kourous-Harrigan

Codrin Cionca

Craig Hammann Stephan

Linjun Zhang

Ankit Girish Vora

Lu Xu

David Allen O'Neil

Cynthia M Neubecker

John Matthew Ginder

John Shutko

Mostafa Parchami

Allan J Lippa

Anthony Gerald King

David James Scholl

Bennie Poindexter

Brian Bennie

Gary Steven Strumolo

Mahendra Somasara Dassanayake

Chaitanya Kumar Narula

Ronald Hugh Miller

Gaurav Pandey

John Anthony Lockwood

Jeff Allen Greenberg

Michael Bryan Vanbelle

Zhen Zhao

Alfred Wasilewski

Calvin Edward Modawell

Marie Therese-Brodner Malecki

Radek Zlamal

Bennie D Poindexter

Michael Anthony Marinolli

Guoyu Lu

A set of first candidate topologies of first candidate roadside infrastructure nodes at respective mounting locations in a geographic area is randomly generated. For each of the first candidate topologies, first simulations, including detection of objects according to selected sensor parameters, installation parameters, and environment parameters for the candidate nodes at the respective mounting locations, are executed. First fitness scores are determined for each of the first candidate topologies by comparing results of the first simulations to ground truth data. Upon identifying one of the first fitness scores as exceeding a threshold, the candidate topology associated with the identified first fitness score is identified for deployment.

Roadside infrastructure deployment

Data is received from a vehicle in a stationary support structure. One or more physical attributes of an infrastructure element is determined based on the vehicle data.

Transportation infrastructure communication and control

In a computer mounted to a stationary support structure a vehicle and an object proximate to the support structure can be detected from data from a sensor mounted to the support structure. A risk condition can be identified based on the detected vehicle and object.

A system includes a light source configured to emit light in a wavelength in a visible range and a wavelength in an infrared range, and a vehicle including an infrared camera, and a computer programmed to identify an object based at least in part on an infrared image from the camera.

Street light with infrared illumination

A system, comprising a processor; and a memory, the memory including instructions to be executed by the processor to acquire a first image of a scene, acquire a second image of the scene while illuminating the scene, identify pixel blooming in a subtracted image determined by subtracting the first image from the second image, remediating the pixel blooming based on empirically determined parameters, and operate a vehicle based on the subtracted image.

Bloom removal for vehicle sensors

Various embodiments of the present disclosure provide systems and methods for provide monitoring a vehicle's surroundings, predicting when a navigation route will pass through a closed road, preemptively modifying the navigation route to avoid the closed road, identifying changes in speed limit, and identifying dangerous debris in the roadway. One embodiment of the camera-based vehicle control system includes a camera configured to obtain image information and a processor configured to: analyze the image information; if a reroute condition is satisfied based on the image information, implement an alternate navigation route; if a threat condition is satisfied based on the image information, cause an output device to output a threat alert; and if a speed condition is satisfied based on the image information, modify a vehicle speed setting.

System and method for controlling vehicle components based on camera-obtained image information

A smart light assembly is provided for a motor vehicle. That smart light assembly includes an electronic sensing device dedicated to the smart light assembly, an adjustable beam light source and a controller directly connected to the adjustable beam light source. A smart lighting system is also disclosed.

Smart light assembly and smart lighting system for a motor vehicle

A heads up display system includes a computer that is programmed to receive data from one or more sources in a vehicle. The computer is further programmed to identify respective subsets of the data to be provided to respective ones of a plurality of display devices, and to provide the respective subsets of the data to the respective display devices.

Vehicle image display

A system and method for displaying information within a vehicle is described. The system includes at least one sensing device detecting a set of data associated with the vehicle, driver, or surroundings, which is processed using a processing unit for identifying a set of information to be displayed. The system also includes a set of display devices coupled to the vehicle. A router selects one or more display devices for displaying the display element based on a set of conditions. A display control unit generates an appropriate display element for the set of information identified and the selected display.

In-vehicle display management system

A vision system () for a vehicle () includes a light source () that generates an illumination beam (). A receiver () generates a first image signal and a second image signal. The first image signal is generated in response to a reflected portion of the illumination beam (). A controller is coupled to the light source () and the receiver (). The controller generates an image in response to the first image signal and the second image signal.

Active night vision image intensity balancing system

A thermal control system () for a light source () of a vision system () includes a heater () that is thermally coupled to the light source (). A thermal sensor () is thermally coupled to the light source () and generates a temperature signal. A controller () is coupled to the heater () and to the thermal sensor () . The controller () operates the heater () when the temperature signal is below a maximum temperature limit. The minimum and maximum temperature limits are obtained from the light-source wavelength-temperature characteristic.

Active night vision thermal control system using wavelength-temperature characteristic of light source

An optical system includes a light sourcedisposed for generating light preferably in a line. An optical elementincludes a stepped reflecting surfacethat includes reflecting surfacesand step surfaces. The reflecting surfaceshave a metalized coating thereon so that the plurality of metalized reflecting surfaces reflect light therefrom. Each reflecting surface is a circular sector that increases in radius from an adjacent circular sector.

Reflective collimation optic

A thermal control system () for a light source () of a vision system () includes a heater () that is thermally coupled to the light source (). A thermal sensor () is thermally coupled to the tight source () and generates a temperature signal. A controller () is coupled to the heater () and to the thermal sensor (). The controller () operates the heater () when the temperature signal is below a maximum temperature limit. The minimum and maximum temperature limits are obtained from the light-source wavelength-temperature characteristic.

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Ypsilanti, MI, United States of America

Jeffrey Thomas Remillard