Ford Global Technolgoies, LLC

Brian Quinn Kettlewell

David Michael Herman

Yashanshu Jain

Ali Husain

Ashwin Arunmozhi

Aaron Lesky

Akshay Vaidya

Geoffrey Horowitz

Akshat Rajvanshi

Kyle B Sorensen

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

The present disclosure relates to a system and a method for addressing an error in a localization system that includes monitoring a plurality of sensors of a driver assistance system in real-time, with each sensor generating a data stream. The method further includes identifying a sensor having an anomalous data stream and calculating a primary localization and a backup localization. The primary localization calculation includes the anomalous data stream and the backup localization calculation does not include the anomalous data stream. Further, the method includes executing an action when the backup localization error estimate exceeds a threshold.

System and method for detecting and addressing errors in a vehicle localization

A computer includes a processor and a memory. The memory stores instructions executable by the processor such that the computer is programmed to input to a trained machine learning program, (1) a sensor fusion error that measures a statistical correlation of data received from a radar sensor and a second sensor in a vehicle, (2) a radar detection range, (3) an amount of reflection from a radar radome, (4) weather data, and (5) aerodynamic data that measures an aerodynamic drag opposing a vehicle motion, and to output from the trained machine learning program a determination concerning a presence of the radar radome.

Vehicle detection of missing body parts

A computer comprises a processor and a memory. The memory stores instructions executable by the processor to, upon detecting an airbag deployment in a vehicle, determine an airbag inflation state based on vehicle sensor data, to adjust a calibration parameter of a vehicle radar sensor based on the determined airbag inflation state, to operate the vehicle radar sensor based on the adjusted calibration parameter, and to update an output characteristic of the radar sensor for operating the vehicle based on the determined airbag inflation state.

Recalibration of radar sensor after airbag deployment

The disclosure is generally directed to systems and methods for sharing a video feed of a camera among multiple image processing components in a vehicle. A first priority may be applied to a first image processing component that performs a first image processing function. A second priority that is lower than the first priority, is applied to a second image processing component that performs a second image processing function. The first function may be deemed more important than the second function due to various reasons. Consequently, the first image processing component is offered priority to apply a first set of camera settings on the camera. The second image processing component may prefer to apply a different set of camera settings for executing the second image processing function. An access arbitrator allows the second image processing component to do so when the first image processing component relinquishes control of the camera.

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Cambridge, Canada

Brian Quinn Kettlewell