Ford Global Technolgoies, LLC

University of Michigan

Codrin Cionca

Juan Enrique Castorena Martinez

Mostafa Parchami

Linjun Zhang

Lu Xu

Zhen Zhao

Jeffrey Thomas Remillard

Helen Elizabeth Kourous-Harrigan

Ankit Girish Vora

Gintaras Vincent Puskorius

Siddharth Agarwal

Roy Clarke

Guoyu Lu

A system includes a computer including a processor and a memory storing instructions executable by the processor to identify a location and an orientation of a vehicle on a map. The instructions include instructions to determine a location of an infrastructure sensor on the map based on the location and the orientation of the vehicle, data from a vehicle sensor, and data from the infrastructure sensor.

Sensor localization from external source data

A sensor system is disclosed. The sensor system may comprise a housing; an emitter, carried by the housing, that emits a beam comprising depth-data signals; a beam-distribution adjustment system; and a processor programmed to control the adjustment system by selectively changing an angular distribution of the depth-data signals emitted from the housing.

Beam distribution adjustment for a sensor

A system is described. The system includes: a support; a first emitter coupled to the support, facing radially outwardly, and configured to emit a continuous-wave (CW) beam; and a second emitter coupled to the support, facing radially outwardly, and configured to emit a pulsed beam. The system may determine reflectivity using the first emitter and may determine a range using the second emitter.

Depth sensor

A computer is programmed to determine a localization of a first vehicle, including location coordinates and an orientation of the first vehicle, based on first vehicle sensor data, and to wirelessly receive localizations of respective second vehicles, wherein a first vehicle field of view at least partially overlaps respective fields of view of each of the second vehicles. The computer is programmed to determine pair-wise localizations for respective pairs of the first vehicle and one of the second vehicles, wherein each of the pair-wise localizations defines a localization of the first vehicle relative to a global coordinate system based on a (a) relative localization of the first vehicle with reference to the respective second vehicle and (b) a second vehicle localization relative to the global coordinate system, and to determine an adjusted localization for the first vehicle that has a minimized sum of distances to the pair-wise localizations.

Self-correcting vehicle localization

A system comprises a computer including a processor, and a memory. The memory stores instructions such that the processor is programmed to determine two or more clusters of vehicle operating parameter values from each of a plurality of vehicles at a location within a time. Determining the two or more clusters includes clustering data from the plurality of vehicles based on proximity to two or more respective means. The processor is further programmed to determine a reportable condition when a mean for a cluster representing a greatest number of vehicles varies from a baseline by more than a threshold.

Detecting changed driving conditions

A system includes a Lidar sensor and a processor. The processor is programmed to detect, using Lidar sensor data, a low-density area comprising a plurality of Lidar beam reflections less than a threshold, to determine dimensions of the area, and to determine that the area represents a physical object based on the detection and determination.

Object detection

A computer, including a processor and a memory, the memory including instructions to be executed by the processor to receive a vehicle path from a server computer and verify the vehicle path based on vehicle dynamics and vehicle constraints. The instruction can include further instructions to, when the vehicle path is verified as correct, operating the vehicle on the vehicle path and, when the vehicle path is verified as incorrect, stopping the vehicle.

Vehicle parking control

A computer includes a processor and a memory. The memory stores instructions executable by the processor to receive, in a vehicle, object data from an external node, and upon identifying a point, in the received object data, that is within a volume defined based on vehicle position data received from a vehicle sensor, to determine an adjusted vehicle position based on the identified point and the vehicle position data.

Localization technique

A map of an area is generated from first sensor data of a first field of view including a blocked area and second sensor data of a second field of view having an unblocked view of the blocked area.

Sensor field of view mapping

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

A computing system receives first sensor data including moving objects and multi-modal LiDAR map data wherein the first sensor is calibrated based on a cost function that alternates between LiDAR map point cloud data and LiDAR map ground reflectivity data as input. The computing system can operate the vehicle based on the moving objects.

Stationary camera localization

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.

Growing community of inventors

Ann Arbor, MI, United States of America

Codrin Cionca