Ford Global Technolgoies, LLC

Clifton Thomas

Shreyasha Paudel

Jinesh J Jain

Jeremy Lerner

Taylor Hawley

Marcos Paul Gerardo Castro

Alexander Groh

Joseph Gifford

Sandhya Bhaskar

A computer, including a processor and a memory, the memory including instructions to be executed by the processor to, based on sensor data in a vehicle, determine a database that includes object data for a plurality of objects, including, for each object, an object identification, a measurement of one or more object attributes, and an uncertainty specifying a probability of correct object identification, for the object identification and the object attributes determined based on the sensor data, wherein the object attributes include an object size, an object shape and an object location. The instructions include further instructions to determine a map based on the database including the respective locations and corresponding uncertainties for the vehicle type and download the map to a vehicle based on the vehicle location and the vehicle type.

Vehicle uncertainty sharing

A vehicle can be operated in an identified map area. Output is obtained from a first classifier, based on input including the map area and one or more current environmental conditions detected in the map area, specifying a first probability that the map area is currently available for sensing to support input-free operation of the vehicle. Then, if and only if the first probability indicates that the map area is currently available for sensing to support input-free operation of the vehicle, output is obtained from a second classifier, based on input including the map area and the one or more current environmental conditions, specifying a second probability that the vehicle will traverse the map area without a minimum risk maneuver event. Then, if and only if the second probability indicates that the vehicle will traverse the map area without a minimum risk maneuver event, operating the vehicle in an input-free mode in the map area.

Location-based vehicle operation

Data from sensors of a vehicle is captured along with data tracking a driver's gaze. The route traveled by the vehicle may also be captured. The driver's gaze is evaluated with respect to the sensor data to determine a feature the driver was focused on. A focus record is created for the feature. Focus records for many drivers may be aggregated to determine a frequency of observation of the feature. A machine learning model may be trained using the focus records to identify a region of interest for a given scenario in order to more quickly identify relevant hazards.

Focus-based tagging of sensor data

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Palo Alto, CA, United States of America

Clifton Thomas