Perceptive Automata in Automotive Artificial Intelligence

Their solution utilizes the full spectrum of insights that humans use to make sophisticated judgments about what a person might do next. Our software is built with a rich amount of data yet is massively scalable and runs faster than real-time using only light compute resources. Perceptive Automata uses behavioral science techniques to train its deep learning models to acquire human like understanding ability. These sophisticated deep learning models output real-time data on the intention, awareness, and other state-of-mind attributes of pedestrians, cyclists, and other motorists. Customers’ autonomous driving systems can then assess the data to understand, for example, whether a stationary pedestrian wants to cross in front of the automated vehicle, if the cyclist ahead of the automated vehicle might want to turn left, or if another vehicle is about to merge lanes.