Perceptive Automata Inc.

Avery Wagner Faller

Samuel English Anthony

Kshitij Misra

A vehicle collects video data of an environment surrounding the vehicle including traffic entities, e.g., pedestrians, bicyclists, or other vehicles. The captured video data is sampled and the sampled video frames are presented to users to provide input on a traffic entity's state of mind. The system determines an attribute value that describes a statistical distribution of user responses for the traffic entity. If the attribute for a sampled video frame is within a threshold of the attribute of another video frame, the system interpolates attribute for a third video frame between the two sampled video frames. Otherwise, the system requests further user input for a video frame captured between the two sampled video frames. The interpolated and/or user based attributes are used to train a machine learning based model that predicts a hidden context of the traffic entity. The trained model is used for navigation of autonomous vehicles.

Generating training datasets for training machine learning based models for predicting behavior of traffic entities for navigating autonomous vehicles

Systems and methods for predicting user interaction with vehicles. A computing device receives an image and a video segment of a road scene, the first at least one of an image and a video segment being taken from a perspective of a participant in the road scene and then generates stimulus data based on the image and the video segment. Stimulus data is transmitted to a user interface and response data is received, which includes at least one of an action and a likelihood of the action corresponding to another participant in the road scene. The computing device aggregates a subset of the plurality of response data to form statistical data and a model is created based on the statistical data. The model is applied to another image or video segment and a prediction of user behavior in the another image or video segment is generated.

System and method of predicting human interaction with vehicles

A vehicle collects video data of an environment surrounding the vehicle including traffic entities, e.g., pedestrians, bicyclists, or other vehicles. The captured video data is sampled and presented to users to provide input on a traffic entity's state of mind. The user responses on the captured video data is used to generate a training dataset. A machine learning based model configured to predict a traffic entity's state of mind is trained with the training dataset. The system determines input video frames and associated dimension attributes for which the model performs poorly. The dimension attributes characterize stimuli and/or an environment shown in the input video frames. The system generates a second training dataset based on video frames that have the dimension attributes for which the model performed poorly. The model is retrained using the second training dataset and provided to an autonomous vehicle to assist with navigation in traffic.

Adaptive sampling of stimuli for training of machine learning based models for predicting hidden context of traffic entities for navigating autonomous vehicles

Machine learning based prediction of human interactions with autonomous vehicles

Display panel of a programmed computer system with a graphical user interface

A computing device receives an image and a video segment of a road scene, the first at least one of an image and a video segment being taken from a perspective of a participant in the road scene and then generates stimulus data based on the image and the video segment. Stimulus data is transmitted to a user interface and response data is received, which includes at least one of an action and a likelihood of the action corresponding to another participant in the road scene. The computing device aggregates a subset of the plurality of response data to form statistical data and a model is created based on the statistical data. The model is applied to another image or video segment and a prediction of user behavior in the another image or video segment is generated.

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Cambridge, MA, United States of America

Avery Wagner Faller