PRI's collaboration with Hecht-Nielsen is expected to reveal an entirely new approach to AI integration with autonomous vehicles of all types. Credit: PRNewsfoto / Perrone Robotic.

Perrone Robotics (PRI) has partnered with Professor Robert Hecht-Nielsen and its research team of the University of California, San Diego’s Vertebrate Movement Laboratory on advanced machine learning methods for autonomous vehicle perception and control.

The new partnership will leverage on Hecht-Nielsen’s work on artificial neural networks (ANN), confabulation theory and vertebrate movement mathematics and PRI’s expertise in autonomous vehicles and robots.

It intends to develop a new framework for PRI’s MAX platform that will apply new learning techniques to MAX-based applications, including driverless car space.

Perrone Robotics founder and CEO Paul Perrone said: “From the earliest days of our MAX platform, we have anticipated and designed in support for machine learning and AI.

“But, this collaboration with the UCSD team will completely extend our existing support.

“From the earliest days of our MAX platform, we have anticipated and designed in support for machine learning and AI.”

“Dr Hecht-Nielsen has very novel and powerful ideas that we believe will compel the entire industry to move forward and, when we successfully harness these concepts, users of the Perrone platform will leverage state-of-the-art for machine learning easily and apply them to their existing solutions.”

With an exclusive access to the project, Perrone Robotics intends to utilise the developments of the project to implement efficient control of driverless vehicles.

Professor Hecht-Nielsen said: “We’ve been interested in Perrone’s work with autonomy for some time; Paul and his team have proven that the MAX platform can be applied in multiple domains to effectively control robotic vehicles.

“We are very excited to work with the Perrone team to make these good solutions even better by applying the insights and techniques our team has developed and we expect to see enhanced autonomy through improved decision-making, perception, and finer-grained control of a given platform.”

The project work will continue through to 2020.