Ford Global Technolgoies, LLC

Argo Ai Gmbh

Bayerische Motoren Werke Aktiengesellschaft

Volkswagen Group of America Investments, LLC

Jelena Frtunikj

Thomas Muehlenstaedt

Daniel Alfonsetti

Rainer Faller

Hans-Ulrich Michel

Zach Kurtz

Jan Martin

Thomas Mühlenstädt

Systems and methods for performing image classification are disclosed. The methods include, by a processor: receiving an input image, generating a label prediction corresponding to the input image using a trained neural network, generating a correlation structure based on a comparison of the input image with each of a plurality of reference images, and generating an updated label prediction corresponding to the input image using the label prediction and the correlation structure.

Systems and methods for image classification using a neural network combined with a correlation structure

Provided are systems, methods, and computer program products for data-driven optimization of onboard data collection, comprising identifying a condition in a roadway associated with an operation of autonomous vehicles (AVs) that may be further optimized to improve performance of one or more AVs; and generating condition capture instructions to communicate to the AVs, wherein the condition capture instructions comprise one or more parameters of a storage request in one or more roadways predicted to exhibit the condition in the roadway; and when at least one instruction in the one or more condition capture instructions is received by an AV, the at least one instruction is configured to cause the AV to collect condition information in the roadway at a time when the condition is predicted to be present in the roadway.

System, method, and computer program product for data-driven optimization of onboard data collection

Systems and methods for selecting data for training a machine learning model using active learning are disclosed. The methods include receiving a plurality of unlabeled sensor data logs corresponding to surroundings of an autonomous vehicle and identifying one or more trends associated with a training dataset comprising a plurality of labeled data logs. The methods also include selecting a subset of the plurality of unlabeled sensor data logs that have an importance score greater than a threshold, the importance score being determined based on the one or more trends. The subset of the plurality of unlabeled sensor data logs is used for updating the machine learning model to generate an updated model.

Systems and methods for intelligent selection of data for building a machine learning model

A fusion system for a motor vehicle includes at least two environment sensors, a neural network coupled to the environment sensors for fusing environment information from the environment sensors, a fusion apparatus for fusing environment information from the environment sensors, and a control device coupled to the neural network and the fusion apparatus. The control device is set up to adapt the environment information fused via the neural network, depending on the environment information fused by the fusion apparatus, and to provide the adapted environment information to a driver assistance system of the motor vehicle.

Fusion system for fusing environment information for a motor vehicle

Systems and methods for selecting data for training a machine learning model using active learning are disclosed. The methods include receiving a plurality of unlabeled sensor data logs corresponding to surroundings of an autonomous vehicle, identifying one or more trends associated with a training dataset comprising a plurality of labeled data logs, determining a function for assigning an importance score to each of the plurality of unlabeled sensor data logs, using the one or more trends, using the function for assigning the importance score to each of the plurality of unlabeled sensor data logs, selecting a subset of the plurality of sensor data logs that have an importance score greater than a threshold, and using the subset of the plurality of sensor data logs for further training the machine learning model trained using the training dataset to generate an updated model.

Systems and methods for on-board selection of data logs for training a machine learning model are provided. The system includes an autonomous vehicle having a plurality of sensors and a processor. The processor receives a plurality of unlabeled images from the plurality of sensors, a machine learning model, and a loss function corresponding to the machine learning model. For each of the plurality of images, the processor then determines one or more predictions using the machine learning model, compute an importance function based on the loss function and the one or more predictions, and transmit that image to a remote server for updating the machine learning model when a value of the importance function is greater than a threshold.

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Bavaria, Germany

Jelena Frtunikj