Abstract: Systems, methods, and computer-readable media are disclosed for systems and methods for intelligent correlation of video and text content using machine learning. Example methods include determining first video content having a first video segment and first text corresponding to the first video segment, generating a first embedded vector using the first video segment and the first text, and determining, using a first machine learning model, a first image relevance score for the first video content based at least in part on the first embedded vector. Example methods may include determining a first text score for the first video content based at least in part on the first embedded vector, determining, using the first image relevance score and the first text score, that the first video segment depicts content of a first undesired category, and causing presentation of a notification indicating presence of content of the first undesired category.

Abstract: An autonomous vehicle collects sensor data of an environment surrounding the autonomous vehicle including traffic entities such as pedestrians, bicyclists, or other vehicles. The sensor data is provided to a machine learning based model along with an expected turn direction of the autonomous vehicle to determine a hidden context attribute of a traffic entity given the expected turn direction of the autonomous vehicle. The hidden context attribute of the traffic entity represents factors that affect the behavior of the traffic entity, and the hidden context attribute is used to predict future behavior of the traffic entity. Instructions to control the autonomous vehicle are generated based on the hidden context attribute.

Abstract: An autonomous vehicle collects sensor data of an environment surrounding the autonomous vehicle including traffic entities such as pedestrians, bicyclists, or other vehicles. The sensor data is provided to a machine learning based model along with an expected turn direction of the autonomous vehicle to determine a hidden context attribute of a traffic entity given the expected turn direction of the autonomous vehicle. The hidden context attribute of the traffic entity represents factors that affect the behavior of the traffic entity, and the hidden context attribute is used to predict future behavior of the traffic entity. Instructions to control the autonomous vehicle are generated based on the hidden context attribute.