Dual IAS Seminar - Associate Professor Sumiko Miyata & Professor Takamichi Miyata

Externally Funded Fellows Associate Professor Sumiko Miyata & Professor Takamichi Miyata each deliver a seminar on their research - Associate Professor Sumiko Miyata - Incentive-Driven AI Networks for Future Road Safety  To achieve fully autonomous driving, "cooperative perception" via V2X (Vehicle-to-Everything) is essential for eliminating blind spots and improving recognition accuracy. However, a major barrier to sustainable implementation lies in ensuring "fair incentives" for participants to share data and computational resources. This seminar introduces an AI-driven network framework designed to balance infrastructure efficiency with participant satisfaction. The presentation first covers a reward distribution mechanism based on the game theory concept of "Nucleolus" to minimize user dissatisfaction within the monitoring system and ensure long-term cooperation. Building on this foundation, the discussion addresses essential network mechanisms for "City as a Service," such as high-speed AI processing that optimizes task offloading between edge servers to minimize communication latency. By integrating incentive design with advanced communication control, it is possible to build a reliable social infrastructure that reduces accidents and optimizes urban mobility. Professor Takamichi Miyata - Multimodal AI that Understands Driver Behaviour without Training Data  Distracted driving remains a critical safety concern, as even brief lapses in attention can lead to serious traffic collisions. Current supervised learning methods require large, labelled datasets and struggle to generalize, while vision-language model (VLM) based methods enable training-free recognition but tend to capture driver identity rather than actual behaviour. This seminar presents a novel framework that overcomes both limitations. The key innovation lies in decoupling identity-related information from behaviour-related cues, combined with refined textual representations to enhance zero-shot recognition robustness across diverse drivers and environments. By integrating decoupled multimodal representations with a lightweight model architecture, the proposed system achieves practical, scalable performance without relying on extensive labelled data. This approach offers a promising pathway toward reliable driver monitoring systems for real-world deployment. For more information about the IAS, please visit - https://www.lboro.ac.uk/research/ias