6G-Enabled Multi-Modal Sensing for Intelligent Transportation

Research Project, 2026 – 2027

6G communication systems will integrate sensing capabilities alongside traditional communication, enabling advanced positioning and mobility estimation of both active devices and passive objects. While communication has long supported transportation applications (e.g., coordination, platooning), the potential of 6G sensing remains unexplored.
 
A key challenge is understanding how and when 6G sensing can complement existing sensors such as radar, LiDAR, and cameras. Multi-modal sensing relies heavily on large AI models (foundation models), but their application to wireless networks remains limited, complicating sensor fusion. Additionally, transmitting and processing large volumes of sensing data can overload 6G networks, increasing latency and posing safety risks.
 
This project aims to develop a wireless foundation model to unify the representation of wireless and other sensing modalities and apply semantic communication for efficient feature compression and fusion. The work is structured into three areas: (i) defining scenarios and KPIs, (ii) developing a wireless foundation model, and (iii) implementing semantic-aware multi-modal data fusion.
 
By advancing high-precision, low-latency sensing, this project will improve real-time decision-making for transportation systems, benefiting traffic management authorities, the automotive industry, and 6G communication stakeholders.