Volkswagen Ag

Dr. Ing. H.c. F. Porsche Aktiengesellschaft

Audi Ag

Porsche Ag

Elnaz Vahedforough

Nikhil J George

Mohak Prafulkumar Bhimani

Joerg Christian Wolf

Pratik Prabhanjan Brahma

Melanie Senn

Jens Langenberg

Muneeb Saleem

Bryan Grant

Samira Pouyanfar

Adrienne Othon

Samuel Zeitvogel

Martin Fest

James Phan Bui

Mikhail Tetelman

Ulrich Maehr

A method is provided. The method includes generating a set of candidate training data based on a simulated environment and a first set of environmental parameters. The method also includes training a machine learning model based on the set of candidate training data. The method further includes obtaining a set of segmentations based on the machine learning model and a set of test data. The method further includes determining whether a mean intersection-over-union (MIOU) of the set of segmentations has increased by more than a threshold change. The method further includes generating a next set of candidate training data based on the simulated environment and a second set of environmental parameters for the simulated environment, in response to determining that the MIOU has increased by more than the threshold MIOU amount.

Generating synthetic training data for perception machine learning models using simulated environments

A powertrain system may determine a power distribution for a set of power sources of a vehicle. The powertrain system may be coupled to a perception system that may provide perception data indicating a scenario, situation, or environment that has been encountered by the vehicle. The powertrain system may also receive health values. The powertrain system may include machine learning model that may generate the power distribution based on one or more of the perception data, the health values, and a power request.

Powertrain control system with state of health information

A powertrain system may determine a power distribution for one or more power sources of a vehicle. The powertrain system may be coupled to a perception system that may provide perception data indicating a scenario, situation, or environment that has been encountered by the vehicle. The powertrain system may include machine learning model that may generate the power distribution based on one or more of the perception data and a power request.

Vehicle powertrain system with machine learning controller

A vehicle attachment for guiding a vehicle includes a magnetic tape sensor, at least one sensor, a controller, at least one coupling device for attachment to the vehicle, and a vehicle interface system. The magnetic tape sensor is configured to detect a path. The at least one sensor can detect whether there are any obstructions along the path. The controller is configured to steer the vehicle along the predefined path and to adjust the speed of the vehicle to avoid coming in contact with any obstructions. The vehicle interface system is configured to communicatively couple the controller to at least one electronic control unit of the vehicle.

Vehicle guiding attachment

A method includes generating a first simulated environment. The first simulated environment includes a route for a simulated vehicle. The method includes determining a set of locations within the first simulated environment for a set of objects. The method includes determining a path for the simulated vehicle based on the route and set of locations. The method includes generating a set of simulated environments based on the first simulated environment and set of locations. The method includes generating a set of images based on the set of simulated environments, the path, and the set of non-deterministically generated objects. The non-deterministically generated objects include unrealistic objects and optionally include realistic objects. The method includes training vehicle guidance models using the set of images which may include abstract or unrealistic objects. The trained vehicle guidance models may be directly used on real vehicles in corresponding real world environments.

Generating training data using simulated environments and training machine learning models for vehicle guidance

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San Jose, CA, United States of America

Elnaz Vahedforough