Ford Global Technolgoies, LLC

Ramchandra Ganesh Karandikar

Scott Vincent Myers

Punarjay Chakravarty

Ashley Elizabeth Micks

Francois Charette

Alexandru Mihai Gurghian

Nikhil Nagraj Rao

Praveen Narayanan

Nikita Jaipuria

Alexander Groh

Ganesh Kumar

A computer, including a processor and a memory, the memory including instructions to be executed by the processor to input a fisheye image to a vector quantized variational autoencoder. The vector quantized variational autoencoder can encode the fisheye image to first latent variables based on an encoder. The vector quantized variational autoencoder can quantize the first latent variables to generate second latent variables based on a dictionary of embeddings. The vector quantized variational autoencoder can decode the second latent variables to a rectified rectilinear image using a decoder and output the rectified rectilinear image.

Image rectification

Based on an image from a stationary sensor, a marker displayed on one or more digital displays on a vehicle is detected. A first location and a first orientation of the marker in a coordinate system is determined by analyzing on pixels in the image. Based on a stationary sensor location and orientation from a map, a second location and a second orientation of the vehicle in the coordinate system is determined.

Vehicle pose detection with fiducial marker

A method for processing audible sounds using ultrasonic sensors. The method includes passively monitoring, via ultrasonic sensors, an external environment for a audible sounds. An audible sound may be detected and used to produce a sound signal. The sound signal may be filtered to determine one or more features corresponding thereto, including a class, a position, and a velocity. A priority may be assigned to the sound signal based on the sound signal to determine an appropriate response. A corresponding system and computer program product are also disclosed and claimed herein.

Passive sound source classification and localization

A vehicle is disclosed that uses data from different types of on-board sensors to determine whether meteorological precipitation is failing near the vehicle. The vehicle may include an on-board camera capturing image data and an on-board accelerometer capturing accelerometer data. The image data may characterize an area in front of or behind the vehicle. The accelerometer data may characterize vibrations of a windshield of the vehicle. An artificial neural network may run on computer hardware carried on-board the vehicle. The artificial neural network may be trained to classify meteorological precipitation in an environment of the vehicle using the image data and the accelerometer data as inputs. The classifications of the artificial neural network may be used to control one or more functions of the vehicle such as windshield-wiper speed, traction-control settings, or the like.

Multi-sensor precipitation-classification apparatus and method

Techniques and implementations pertaining to detection of moving objects, such as pedestrians, when a vehicle moves in a rearward direction are described. A method may involve identifying a region of interest when a vehicle moves in a rearward direction. The method may involve detecting a moving object in the region of interest. The method may also involve determining whether a collision with the moving object by the vehicle moving in the rearward direction is likely. The method may further involve providing a human-perceivable signal responsive to a determination that the collision is likely.

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Palo Alto, CA, United States of America

Ramchandra Ganesh Karandikar