Self-driving cars learn to share road knowledge through digital word-of-mouth

An NYU Tandon-led research team has developed a way for self-driving vehicles to share their knowledge about road conditions indirectly, making it possible for each vehicle to learn from the experiences of others even when they rarely meet on the road.

The research, to be presented in a paper at the Association for the Advancement of Artificial Intelligence Conference (AAAI 2025) on February 27, 2025, tackles a persistent problem in artificial intelligence: how to help vehicles learn from each other while keeping their data private. The paper is available on the arXiv preprint server.

Typically, vehicles only share what they have learned during brief direct encounters, limiting how quickly they can adapt to new conditions.

“Think of it like creating a network of shared experiences for self-driving cars,” said Yong Liu, who supervised the research led by his Ph.D. student Xiaoyu Wang. Liu is a professor in NYU Tandon’s Electrical and Computer Engineering Department and a member of its Center for Advanced Technology in Telecommunications and Distributed Information Systems and of NYU WIRELESS.

“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. This would make every vehicle smarter and better prepared for situations it hasn’t personally encountered,” Liu said.

The researchers call their new approach Cached Decentralized Federated Learning (Cached-DFL). Unlike traditional Federated Learning, which relies on a central server to coordinate updates, Cached-DFL enables vehicles to train their own AI models locally and share those models with others directly.

When vehicles come within 100 meters of each other, they use high-speed device-to-device communication to exchange trained models rather than raw data. Crucially, they can also pass along models they’ve received from previous encounters, allowing information to spread far beyond immediate interactions. Each vehicle maintains a cache of up to 10 external models and updates its AI every 120 seconds.

To prevent outdated information from degrading performance, the system automatically removes older models based on a staleness threshold, ensuring that vehicles prioritize recent and relevant knowledge.

The researchers tested their system through computer simulations using Manhattan’s street layout as a template. In their experiments, virtual vehicles moved along the city’s grid at about 14 meters per second, making turns at intersections based on probability, with a 50% chance of continuing straight and equal odds of turning onto other available roads.

Unlike conventional decentralized learning methods, which suffer when vehicles don’t meet frequently, Cached-DFL allows models to travel indirectly through the network, much like how messages spread in delay-tolerant networks, which are designed to handle intermittent connectivity by storing and forwarding data until a connection is available. By acting as relays, vehicles can pass along knowledge even if they never personally experience certain conditions.

“It’s a bit like how information spreads in social networks,” explained Liu. “Devices can now pass along knowledge from others they’ve met, even if those devices never directly encounter each other.”

This multi-hop transfer mechanism reduces the limitations of traditional model-sharing approaches, which rely on immediate, one-to-one exchanges. By allowing vehicles to act as relays, Cached-DFL enables learning to propagate across an entire fleet more efficiently than if each vehicle were limited to direct interactions alone.

The technology allows connected vehicles to learn about road conditions, signals, and obstacles while keeping data private. This is especially useful in cities where cars face varied conditions but rarely meet long enough for traditional learning methods.

The study shows that vehicle speed, cache size, and model expiration impact learning efficiency. Faster speeds and frequent communication improve results, while outdated models reduce accuracy. A group-based caching strategy further enhances learning by prioritizing diverse models from different areas rather than just the latest ones.

As AI moves from centralized servers to edge devices, Cached-DFL provides a secure and efficient way for self-driving cars to learn collectively, making them smarter and more adaptive. Cached-DFL can also be applied to other networked systems of smart mobile agents, such as drones, robots and satellites, for robust and efficient decentralized learning towards achieving swarm intelligence.