Audi Ag

Volkswagen Ag

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

Porsche Ag

Muneeb Saleem

Nikhil J George

Martin Hempel

Premkumar Natarajan

Somudro Gupta

Elnaz Vahedforough

Samira Pouyanfar

Samuel Zeitvogel

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

Systems, devices, and methodologies are provided for training and controlling a neural network. The neural network is trained using definitive and random training modes to train neurons in a monolithic network. The neural network output is used to control an autonomous or semi-autonomous vehicle.

Systems and methods for training and controlling an artificial neural network with discrete vehicle driving commands

Pedestrian activity recognition is embodied in a method, system, non-transitory computer-readable and vehicle. A Siamese neural network is trained to recognize a plurality of pedestrian activities by training it recordings of the same pedestrian activity from two or more separate training image capture devices. The Siamese neural network is deployed with continual data collection from an additional image capture device to create a dataset of clusters of similar activities in an unsupervised manner. A spatio-temporal intent prediction model is then trained that can be deployed to recognize and predict pedestrian activity. Based on the likelihood of a particular pedestrian activity occurring or currently being underway, an automatic vehicle maneuver can be executed to navigate the situation.

Method of pedestrian activity recognition using limited data and meta-learning

In a method and system for generating vehicle image data, and to improve the generation of vehicle image data, 2D object information having at least 2D object data and object location data of one or more objects, perspective vehicle image data, and vehicle image location data for at least a portion of said vehicle image data are obtained. The object location data is compared with said vehicle image location data; and in case said object location data corresponds to said image location data, said 2D object data is correlated with said perspective vehicle image data using the object location data and the vehicle image location data to obtain a segmented vehicle image dataset.

Method of generating segmented vehicle image data, corresponding system, and vehicle

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Foster City, CA, United States of America

Muneeb Saleem