Ford Global Technolgoies, LLC

Aaron Lesky

David Michael Herman

Ronald Beras

Ashwin Arunmozhi

Venkatesh Krishnan

Catherine M Amodeo

Geoffrey Horowitz

Brian Quinn Kettlewell

Akshat Rajvanshi

Alexander George Shanku

Keith Van Gorder

A system includes a processor programmed to: identify, based on data from first sensors included in a first vehicle, a second vehicle moving within a first threshold distance of a travel path of the first vehicle and within a second threshold distance of the first vehicle. The processor is further programmed to receive, from second sensors included in the first vehicle, transient velocity data of the second vehicle; and determine an operating condition of the second vehicle based on the transient velocity data.

Detection of vehicle operating conditions

A system for detecting a road surface includes a processor programmed to identify, in a vehicle sensor field of view, environment features including a sky, a road surface, a road shoulder, based on map data and data received from the one or more vehicle sensors, upon determining a low confidence in identifying an area of the field of view to be a road surface or sky, to receive a polarimetric image, and to determine, based on the received polarimetric image, whether the identified area is a mirage phenomenon, a wet road surface, or the sky. The low confidence is determined upon determining that a road surface has a vanishing edge, a road lane marker is missing, or an anomalous object is present.

Vehicle sensor occlusion detection

A computer includes a processor and a memory storing instructions executable by the processor to receive a first time series of positions of a vehicle sensor mounted on a vehicle from at least one calibration sensor spaced from the vehicle, measured while vibrations are applied to the vehicle; receive a second time series of motion of the vehicle sensor based on data from the vehicle sensor measured while the vibrations are applied to the vehicle; fuse the first and second time series; and determine at least one calibration value for an adjustment model based on the fusion of the first and second time series. The adjustment model is a model of motion of the vehicle sensor relative to the vehicle resulting from motion of the vehicle.

Dynamic calibration of vehicle sensors

A computer, including a processor and a memory, the memory including instructions to be executed by the processor to obtain velocity lidar point cloud data acquired with a frequency modulated continuous wave (FMCW) lidar sensor, wherein the velocity lidar point cloud data includes a speed with which a data point is moving with respect to the FMCW lidar sensor, filter the velocity lidar point cloud data to select static velocity data points, wherein the static velocity data points are velocity data points each correspond to a point on a roadway around a vehicle. The instructions can include further instructions to determine FMCW lidar sensor accelerations in six degrees of freedom based on the static velocity lidar data points and determine FMCW lidar sensor rotations and translations in six degrees of freedom based on the FMCW lidar sensor accelerations in six degrees of freedom. The instructions can include further instructions to determine vehicle rotations and translations in six degrees of freedom based on inertial measurement unit (IMU) data, determine FMCW lidar sensor mis-alignment based on comparing the FMCW lidar sensor rotations and translations with the vehicle rotations and translations and align the FMCW lidar sensor based on the FMCW lidar sensor mis-alignment. The instructions can include further instructions to operate a vehicle based on the aligned FMCW lidar sensor.

Vehicle sensor fusion

A computer includes a processor and a memory storing instructions executable by the processor to input strain data measuring mechanical strain on a vehicle camera and operation data of a vehicle component, the operation data describing at least one of an output or a state of the vehicle component, to a machine learning program that outputs a displacement of the vehicle camera from a neutral position based on the strain data and the operation data. The instructions further include instructions to identify a transformation matrix that transforms data from the vehicle camera to a coordinate system of the vehicle camera in the neutral position based on the output displacement of the vehicle camera, to apply the transformation matrix to data collected by the vehicle camera to generate transformed data, and to actuate one of the vehicle component or a second vehicle component based on the transformed data.

Vehicle sensor operation

A system includes a computer including a processor programmed to identify, based on data from first sensors included in a first vehicle, that a second vehicle proximate to a route of the first vehicle is stationary. The processor is further programmed to receive, from second sensors included in the first vehicle, transient velocity data from the second vehicle; and determine an operating condition of the second vehicle based on the transient velocity data.

The present disclosure discloses a system and a method for mitigating image distortion. In an example implementation, the system and the method can receive vehicle state data and vehicle inertial measurement data; generate an image distortion prediction indicative of image distortion within an image captured by the image capture assembly based on the vehicle state data and the vehicle inertial measurement data; and at least one of correct or mitigate the image distortion based on the image distortion prediction.

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Ypsilanti, MI, United States of America

Aaron Lesky