Perceptive Automata Inc.

Jeffrey D Zaremba

Samuel English Anthony

Till S Hartmann

A system uses a machine learning based model to determine attributes describing states of mind and behavior of traffic entities in video frames captured by an autonomous vehicle. The system classifies video frames according to traffic scenarios depicted, where each scenario is associated with a filter based on vehicle attributes, traffic attributes, and road attributes. The system identifies a set of video frames associated with ground truth scenarios for validating the accuracy of the machine learning based model and predicts attributes of traffic entities in the video frames. The system analyzes video frames captured after the set of video frames to determine actual attributes of the traffic entities. Based on a comparison of the predicted attributes and actual attributes, the system determines a likelihood of the machine learning based model making accurate predictions and uses the likelihood to generate a navigation action table for controlling the autonomous vehicle.

Scenario identification for validation and training of machine learning based models for autonomous vehicles

Ground truth based metrics for evaluation of machine learning based models for predicting attributes of traffic entities for navigating autonomous vehicles

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Cambridge, MA, United States of America

Jeffrey D Zaremba