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Improved wireless communication can boost launch of autonomous vehicles

Improved wireless communication can boost launch of autonomous vehicles

The project FLASH (Federated Learning Supporting Efficient and Reliable Inference over Vehicular Networks) aims to accelerate the development of autonomous vehicles by merging machine learning with cellular networks. Led by Professor Carlo Fischione from KTH and Professor H. Vincent Poor from Princeton University, the project will develop learning algorithms to improve prediction capabilities and meet the reliability requirements of self-driving cars. By applying federated learning, which enables cars to learn from each other using distributed data from their sensors, the project aims to enhance the ability of autonomous vehicles to predict the movements of other vehicles, pedestrians, and cyclists. The integration of federated learning with cellular communication networks is a promising area due to advancements in distributed machine learning and the deployment of 5G networks. The project's goals include improving energy efficiency, reducing emissions and fuel consumption, and enhancing real-time decision-making to minimize traffic accidents and fatalities. Dr. Mairton Barros, working on the project, anticipates fruitful collaborations with colleagues from Princeton University, leading to novel research in integrating federated learning and vehicular communications.

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