Massachusetts Patent of the Month – March 2023
Perceptive Automata Inc is rebuilding the way autonomous vehicles navigate through traffic with their innovative machine learning-based models. Autonomous vehicles rely heavily on sensors to perceive their surroundings and avoid obstacles, and the accuracy of these sensors is crucial for the safety of passengers and other road users.
However, conventional techniques for predicting the motion of non-stationary objects, such as pedestrians and bicyclists, are often inaccurate. This is where Perceptive Automata comes in. Their system generates machine learning-based models that predict hidden context attributes associated with traffic entities.
For example, the hidden context may represent a state of mind or task that a pedestrian is planning on accomplishing. The machine learning-based model is trained to receive an image of traffic as input and generate an output representing hidden context for a traffic entity displayed in the image.
To train their machine learning-based model, Perceptive Automata uses a training data set that includes video frames and user responses describing hidden context attributes associated with the video frames. The machine learning-based model is then adjusted to minimize the loss function, resulting in a highly accurate prediction of hidden context attributes.
This innovative technology has a wide range of applications, including navigating autonomous vehicles through traffic. With Perceptive Automata’s machine learning-based models, autonomous vehicles can accurately predict the behavior of pedestrians and other non-stationary objects, ensuring the safety of passengers and other road users.
Perceptive Automata Inc’s innovative technology is revolutionizing the way autonomous vehicles navigate through traffic. By predicting hidden context attributes associated with traffic entities, their machine learning-based models ensure the safety of passengers and other road users.
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