SOURCE-LINKED INTELLIGENCE

Reinventing Multiterminal Coding for Intelligent Machines

CORDIS · observation · Publication date unknown

Reinventing Multiterminal Coding for Intelligent Machines Advancements in sensors and deep learning have elevated the perception capacity of machines, bringing mid-level autonomy within reach. However, the abundance of high-dimensional data, including video and dynamic point cloud streams, strains current storage and communication technologies to their limits and curtails the ability of machines to collaboratively perceive the environment, a critical factor for achieving safety and the ambitious goal of high-level autonomy. State-of-the-art cooperative perception methods are based purely on a data-driven approach, requiring massive training data and computational resources, and lacking interpretability, explainability, and a solid theoretical foundation. This proposal puts forth a groundbreaking multiterminal coding paradigm for intelligent machines enabling data compression and communication systems that break the current limits of the predictive coding archetype. It bui

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recordType

award

status

SIGNED

region

EU

value

1999403.75

unit

EUR