Ford Global Technolgoies, LLC

Kyu Jeong Han

Jinesh J Jain

Harpreetsingh Banvait

Ashley Elizabeth Micks

Shane Elwart

Scott Vincent Myers

Gintaras Vincent Puskorius

Bibhrajit Halder

Jonathan Thomas Mullen

Walter Joseph Talamonti

Zachary David Nelson

Daniel Levine

Tory Smith

A machine learning model is trained by defining a scenario including models of vehicles and a typical driving environment. A model of a subject vehicle is added to the scenario and sensor locations are defined on the subject vehicle. A perception of the scenario by sensors at the sensor locations is simulated. The scenario further includes a model of a lane-splitting vehicle. The location of the lane-splitting vehicle and the simulated outputs of the sensors perceiving the scenario are input to a machine learning algorithm that trains a model to detect the location of a lane-splitting vehicle based on the sensor outputs. A vehicle controller then incorporates the machine learning model and estimates the presence and/or location of a lane-splitting vehicle based on actual sensor outputs input to the machine learning model.

Training algorithm for collision avoidance

Methods, devices, and systems pertaining to in-vehicle tutorials are described. A method may involve receiving a request for an in-vehicle tutorial of an operational feature of a vehicle from a user and simulating expected driving behavior corresponding to the operational feature in the vehicle. The method may further include monitoring operational behavior of the user, comparing the operational behavior with the expected driving behavior, and providing a feedback to the user based on the comparison.

In-vehicle tutorial

A machine learning model is trained by defining a scenario including models of vehicles and a typical driving environment. A model of a subject vehicle is added to the scenario and sensor locations are defined on the subject vehicle. A perception of the scenario by sensors at the sensor locations is simulated. The scenario further includes a model of a parked vehicle with its engine running. The location of the parked vehicle and the simulated outputs of the sensors perceiving the scenario are input to a machine learning algorithm that trains a model to detect the location of the parked vehicle based on the sensor outputs. A vehicle controller then incorporates the machine learning model and estimates the presence and/or location of a parked vehicle with its engine running based on actual sensor outputs input to the machine learning model.

Training algorithm for collision avoidance using auditory data

A controller for an autonomous vehicle receives audio signals from one or more microphones. The audio signals are input to a machine learning model that classifies the source of the audio features. For example, features may be classified as originating from a vehicle. A direction to a source of the audio features is determined based on relative delays of the audio features in signals from multiple microphones. Where audio features are classified with an above-threshold confidence as originating from a vehicle, collision avoidance is performed with respect to the direction to the source of the audio features. The direction to the source of the audio features may be correlated with vehicle images and/or map data to increase a confidence score that the source of the audio features is a parked vehicle with its engine running. Collision avoidance may then be performed with potential paths of the parked vehicle.

Collision avoidance using auditory data

A controller for an autonomous vehicle receives audio signals from one or more microphones. The outputs of the microphones are pre-processed to enhance audio features that originated from vehicles. The outputs may also be processed to remove noise. The audio features are input to a machine learning model that classifies the source of the audio features. For example, features may be classified as originating from a vehicle. A direction to a source of the audio features is determined based on relative delays of the audio features in signals from multiple microphones. Where audio features are classified with an above-threshold confidence as originating from a vehicle, collision avoidance is performed with respect to the direction to the source of the audio features.

The present invention extends to methods, systems, and computer program products for identifying a person as a driver of a vehicle. Aspects include using in-vehicle sensors to increase the accuracy of driver identification initially determined using other mechanisms. As such, at least two different types of sensory devices can be utilized to gather data in an effort to identify the driver. The data gathered from the first sensor (e.g., at a key fob) is processed to identify the driver based on learned characteristic patterns, such as the driver's gait, before the driver enters the vehicle. The data gathered from the second sensor (e.g., in an in-vehicle face or voice recognition system) is processed to confirm the driver. The confirmation is based on biometric data and learned characteristic patterns. Data from the second sensor is provided back to the first sensor to confirm the driver identity.

Identifying a driver of a vehicle

A controller for an autonomous vehicle received audio streams from one or more microphones and one or more image streams from one or more cameras. The audio streams are processed to identify audio features that potentially correspond to a motorcycle. The video streams are likewise processed to identify image objects that have movement and/or visual attributes corresponding to a motorcycle. The audio features and image objects are correlated. If a correlation is identified among the audio features and image objects, then a potentially lane-splitting motorcycle may be determined to be proximate. Actions may be taken to avoid collision with the lane-splitting motorcycle, such as avoiding lane changes, signaling longer, or performing lane changes more slowly.

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Palo Alto, CA, United States of America

Kyu Jeong Han