Dual IAS Seminar - Associate Prof. Sumiko Miyata & Prof. 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.

Arrivals from 11:45 am for a 12:00 noon start. For those joining in-person, lunch will be served after the seminar from 1:00pm.

International House can be found here on the campus map.

If these in-person tickets have sold out, you can still join online by registering for the Teams Webinar.

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Host or Publisher Loughborough Institute of Advanced Studies