Project description
Intelligent information sharing for cooperative autonomous machines
The rise of advanced sensors and deep learning has improved machine perception. However, the massive flow of high-dimensional data, such as video and dynamic point clouds, pushes current storage and communication technologies to their limits. This overload hinders machines from collaborating effectively, an essential step towards safe, high-level autonomy. Current cooperative perception methods rely solely on data-driven models, requiring vast training datasets and computational resources, while lacking interpretability and theoretical grounding. The ERC-funded IONIAN project unites traditional multiterminal source coding and signal processing with modern interpretable and explainable artificial intelligence. Its goal is to enhance data compression and communication for intelligent machines. This will enable autonomous systems, from vehicles and drones to robots, to safely perceive their environment.
Objective
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.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
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CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
This project's classification has been validated by the project's team.
Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-ERC - HORIZON ERC Grants
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2024-COG
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
1050 BRUSSEL
Belgium
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.