Massachusetts Patent of the Month – February 2023
Human drivers have a natural ability to analyze their surroundings and predict behaviors of pedestrians, bicyclists, and other vehicles. Replicating this cognitive ability in autonomous vehicles has not been easy. Perceptive Automata Inc., is tackling this problem, creating the technology needed to predict and visualize user interaction of traffic entities with autonomous vehicles.
The company has recently patented their machine learning solution which predicts human interactions. The technology is designed to help autonomous vehicles better understand and predict the behavior of people in complex urban environments.
Traditional AI treats humans and other traffic entities like a black box, with no way of predicting their behavior. Perceptive Automata’s solution changes the way machine learning is trained, switching from predicting human movement to predicting their state of mind.
To achieve this capability, the company takes recording from vehicles that show interactions with people. These videos offer a data-rich source of information since every time a person appears and interacts with a vehicle, they give off hundreds of signals that indicate their state of mind. Human drivers read these signals to predict what a pedestrian might do next. The technology uses these videos to build an understanding of these signals so that AI might read them as well.
The videos are accompanied by feedback from humans who have reviewed random slices and images from the video, reporting what they think the subject’s state of mind might be. For instance, a pedestrian with a hand up – are they waving the car on or thanking the car for letting them cross? This process is repeated hundreds of thousands of times, building a massive database of interactions and signals which is then used to train and inform autonomous vehicles.
The technology is impressive and has potential for improving the safety of autonomous vehicles. Perceptive Automata is changing the way AI-driven cars are programmed, making it possible for them to predict and react to the behavior of people in complex urban environments.
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