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 builds a unique concept that unifies traditional distributed source coding and signal processing domain knowledge with modern deep learning.

First, it leverages machine learning to solve long-standing problems in multiterminal coding theory and devise code constructions achieving the fundamental limits, thereby establishing a theoretical framework that defines the amount of information required to be sent per agent to solve the cooperative perception task.

Second, it leverages domain knowledge to drive the design of interpretable and data- and parameter-efficient machine learning models for cooperative perception.

Third, it reinforces this interplay by pioneering explanations that enforce and assess the interpretability of the designed models.

IONIAN will have a profound impact on the way intelligent machines, including ground and aerial vehicles, and mobile robots, compress and communicate multi-sensory data to collaboratively perceive the environment for autonomous safe navigation, ultimately leading to trustworthy operation and acceptance of such systems.