ITEA CLEAR: Comprehensive Learning for Enhanced AI Responsiveness
| Reference number | |
| Coordinator | ALSTOM Rail Sweden AB |
| Funding from Vinnova | SEK 14 987 334 |
| Project duration | November 2025 - November 2028 |
| Status | Ongoing |
| Venture | Eureka cluster co-funding |
Purpose and goal
The CLEAR project aims to address the growing challenges in integrating diverse, multimodal data into industrial AI systems and improving the reliability of their outputs and decisions. By leveraging advanced AI techniques and context-aware capabilities, CLEAR will boost and capitalize on the capabilities of Large Multi-Modal Models (LMMs) and Large Language Models (LLMs) to efficiently manage complex data inputs.
Expected effects and result
CLEAR innovations are expected to reduce operational costs, increase safety, and improve system reliability across sectors such as transportation, agriculture, manufacturing, and telecommunications. At its core, CLEAR seeks to tackle the limitations of current AI systems for industrial applications in processing a wide range of real-time, multi-modal data —including satellite and on-ground visual data, emergency response, geospatial, and time-series data.
Planned approach and implementation
To achieve the project´s goals, a strong international consortium of industry and research partners with complementary expertise has been formed. The project work is structured in six work packages: establishment of industrial use cases and demonstrators (WP1), data aggregation and fusion (WP2), model construction, fine-tuning and benchmarking (WP3), CLEAR multimodal pipelines (WP4), dissemination and exploitation (WP5) and project management (WP6). WP3 and WP6 are led by the Swedish consortium.