Session Code: e3jy5
Abstract:
This open invited track aims to explore the design and deployment of Cyber-Physical-Human Systems (CPHS) to generate both individual benefits and broader societal impact. We welcome contributions that enhance personal utility, autonomy, and well-being through human-centric system design, while also addressing collective challenges such as sustainability, equity, and resilience. This track emphasizes interdisciplinary approaches that integrate control theory with human factors, artificial intelligence (AI), and machine learning, cognitive psychology, ergonomic engineering, and optimization techniques.
Co-Sponsors:
SICE TC on Cyber-Physical-Human Systems
Keywords from TC 9.2:
Cyber-physical and human systems (CPHS)
System dynamics and control in CPHS
Human-centric automation/AI Systems, and human agency
Social networks and opinion dynamics
Cognitive and emotional control/AI systems, arts and control
Safety-critical and resilient systems
Explicability and transparency in CPHS
Detailed description of the topic:
This open invited track will focus on modeling, design, analysis, verification, and certification of CPHS, which encompasses theoretical, algorithmic, computational, and experimental perspectives. Topics of interest include, but are not limited to:
Modeling, analysis, and control of integrated CPHS
Social and societal aspects of CPHS
Security, privacy and ethics in CPHS
Human behavior and teaming between humans and autonomy
Safety-critical and resilient CPHS
CPHS applications in healthcare, transportation, human-space technology, and smart infrastructure.Â
In addition, we welcome submissions on emerging and concrete applications such as:
Control and planning support for drivers and operators using generative AI, such as large language models (LLMs)
Teleoperation systems that support human operators through multimodal feedback (e.g., visual, haptic, auditory)
Interactive robots or autonomous vehicles that adapt to human intention, emotion, or cognitive state in real time
Personalized health monitoring and intervention using wearable sensors and human-in-the-loop control
Traffic flow control using behavioral nudges or incentive mechanisms to induce driver behavior change and reduce congestion
Demand prediction in energy or transportation systems using machine learning and foundation models
Multi-agent coordination in smart cities where both human preferences and physical constraints are integrated
This track seeks contributions that explore how CPHS can enhance both individual decision-making and system-level performance, enabling safe, efficient, and socially responsible integration of humans and machines.
Organizers (tentative):
Institute of Science Tokyo, Japan
University of Southern California, USA
Keio University, Japan
Politecnico di Torino, Italy