Argo Ai Gmbh

Volkswagen Group of America Investments, LLC

Ford Global Technolgoies, LLC

Thomas Muehlenstaedt

Jelena Frtunikj

Roman Nagy

Jost Poettner

Yunxin Gu

Zach Kurtz

Vincent Aravantinos

Disclosed herein are system, method, and computer program product embodiments for generating and refining simulation scenarios. For example, the method includes generating multiple base scenarios, each including one or more constant and one or more variable parameters. For each of the base scenarios, the method includes generating scenario variations, each of which is associated with a unique combination of values assigned to its base scenario's parameters. The method further includes determining a system boundary in a parameter space defined by the variable parameters, wherein the system boundary divides the parameter space into a region including successful scenario variations and a region including unsuccessful scenario variations, and generating additional scenario variations within a threshold distance of the system boundary. The method further includes simulating operation of an autonomous vehicle (AV) using one or more generated scenario variations.

Automated generation and refinement of variation parameters for simulation scenarios

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

Disclosed herein are system, method, and computer program product embodiments for detecting anomalies during software testing. The methods include generating a plurality of test reports for the software program by executing one or more test cases on a plurality of versions of the software program, generating a control chart based on the plurality of test reports, generating an alert when at least one testing characteristic includes an anomaly over the plurality of versions of the software program as determined based on the control chart. The control chart includes a plot associated with at least one testing characteristic of the software program, and a historical context associated with execution of the one or more test cases on the plurality of versions of the software program.

Systems and methods for monitoring progression of software versions and detection of anomalies

Methods of identifying corner case simulation scenarios that are used to train an autonomous vehicle motion planning model are disclosed. A system selects a scene that includes data captured by one or more vehicles over a time period. The data includes one or more actors that the vehicle's sensors perceived over the time period in a real-world environment. The system selects a scene that includes a safety threshold violation, and it identifies the trajectory of an actor that participated in the violation. The system generates simulated scenes that alter the trajectory of the actor in the selected scene, selects simulated scenes that are more likely to occur in the real world and that may include safety threshold violations that go beyond any that may be found in the original scene, and uses the selected simulated scenes to train an autonomous vehicle motion planning model.

Rare event simulation in autonomous vehicle motion planning

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.

Growing community of inventors

Bavaria, Germany

Thomas Muehlenstaedt