Ford Global Technolgoies, LLC

Graham Mills

Praveen Narayanan

Sarah Houts

Punarjay Chakravarty

Gaurav Pandey

Tyler Reid

Shreyasha Paudel

Example localization systems and methods are described. In one implementation, a method receives a camera image from a vehicle camera and cleans the camera image using a VAE-GAN (variational autoencoder combined with a generative adversarial network) algorithm. The method further receives a vector map related to an area proximate the vehicle and generates a synthetic image based on the vector map. The method then localizes the vehicle based on the cleaned camera image and the synthetic image.

Localization systems and methods

Systems, methods, and devices for determining a location of a vehicle or other device are disclosed. A method includes receiving sensor data from a sensor and determining a prior map comprising LIDAR intensity values. The method includes extracting a sub-region of the prior map around a hypothesis position of the sensor. The method includes extracting a Gaussian Mixture Model (GMM) distribution of intensity values for a region of the sensor data by expectation-maximization and calculating a log-likelihood for the sub-region of the prior map based on the GMM distribution of intensity values for the sensor data.

Localization for autonomous vehicles using gaussian mixture models

Vehicle localization systems and methods

A system, comprising a processor; and a memory, the memory storing instructions executable by the processor to receive image data from a vehicle sensor installed in a vehicle, transform the image data into global coordinate data, estimate fractal dimension values of the global coordinate data, and determine calibrated parameters based on the fractal dimension values to operate the vehicle sensor.

Vehicle sensor operation

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San Francisco, CA, United States of America

Graham Mills