New chapter for the book of the OCDDC 2025 accepted

Shang Gao published a book chapter for the Organic Computing -- Doctoral Dissertation Colloquium 2025 entitled Dynamic Multimodal Cyclist Behaviour Modelling: From Representation Insights to Federated Collaboration.

Abstract:

In the field of intelligent transportation systems, accurately modelling cyclist behaviour is crucial to enhancing traffic safety and efficiency, particularly as road environments are increasingly shared with automated vehicles. Modelling cyclist behaviour is intrinsically complex due to its dynamic, context-dependent nature, shaped by diverse environmental and individual factors. These complexities pose significant challenges for traditional centralized learning approaches, particularly regarding data privacy concerns and heterogeneity across distributed data sources. Federated learning, as an emerging framework, offers the potential to address these challenges by enabling collaborative model training without sharing raw data. However, in real-world traffic scenarios, federated learning must tackle the complexities of non-independent and identically distributed data and the need for real-time adaptability in dynamic traffic environments. This work explores an uncertainty-aware federated learning framework, incorporating multimodal data fusion, to support modelling cyclist behaviour in traffic environments. This research addresses key challenges in federated learning for cyclist behaviour modelling and explores potential solutions, offering both theoretical and practical insights for future applications in intelligent transportation sys- tems.