Researchers have discovered a way for self-driving cars to freely share information while on the road without the need to establish direct connections.

“Cached Decentralized Federated Learning” (Cached-DFL) is an artificial intelligence (AI) model sharing framework for self-driving cars that allow them to pass each other and share accurate and recent information. This information includes the latest ways to handle navigation challenges, traffic patterns, road conditions, and traffic signs and signals.

Usually, cars have to be virtually next to each other and grant permissions to share driving insights they’ve collected during their travels. With Cached-DFL, however, scientists have created a quasi-social network where cars can view each other’s profile page of driving discoveries — all without sharing the driver’s personal information or driving patterns.

Self-driving vehicles currently use data stored in one central location, which also increases the chances of large data breaches. The Cached-DFL system enables vehicles to carry data in trained AI models in which they store information about driving conditions and scenarios. “Think of it like creating a network of shared experiences for self-driving cars,” wrote Dr. Yong Liu, the project’s research supervisor and engineering professor at NYU’s Tandon School of Engineering. “A car that has only driven in Manhattan could now learn about road conditions in Brooklyn from other vehicles, even if it never drives there itself.”

The cars can share how they handle scenarios similar to those in Brooklyn that would show up on roads in other areas. For instance, if Brooklyn has oval-shaped potholes, the cars can share how to handle oval potholes no matter where they are in the world.

The scientists uploaded their study to the preprint arXiv database and presented their findings at the Association for the Advancement of Artificial Intelligence Conference. Through a series of tests, the scientists found that quick, frequent communications between self-driving cars improved the efficiency and accuracy of driving data.

The scientists placed 100 virtual self-driving cars into a simulated version of Manhattan and set them to “drive” in a semi-random pattern. Each car had 10 AI models that updated every 120 seconds, which is where the cached portion of the experiment emerged. The cars hold on to data and wait to share it until they have a proper vehicle-to-vehicle (V2V) connection to do so. This differs from traditional self-driving car data-sharing models, which are immediate and allow no storage or caching.