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DTSTART:19701025T020000
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DTSTAMP:20260718T054105Z
DTSTART;TZID=Europe/London:20260713T110000
DTEND;TZID=Europe/London:20260713T120000
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SUMMARY:Seminar with Dr Sumiko Miyata and Professor Takamichi Miyata
DESCRIPTION:Associate Professor Sumiko Miyata will present 'Incentive-driven AI networks for future road safety'.    Abstract: 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 will present 'Multimodal AI that Understands Driver Behaviour without Training Data'.    Abstract: 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. https://events.st-andrews.ac.uk/events/seminar-with-dr-sumiko-miyata-and-prof-takamichi-miyata/
LOCATION:Jack Cole Building, North Haugh, KY16 9SX, St Andrews, Fife, Scotland
URL:https://events.st-andrews.ac.uk/events/seminar-with-dr-sumiko-miyata-and-prof-takamichi-miyata/
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