Situational awareness is a critical component of effective collaboration between humans and machines in a variety of contexts, ranging from manufacturing and transportation, to healthcare and emergency response. Situational awareness refers to the ability of individuals and teams to understand and interpret the current state of a particular system or environment, including factors such as the location and status of equipment, the behaviour of other team members, and the overall context and objectives of the task at hand. In the context of human-machine collaborative tasks, situational awareness is particularly important, as it can help to reduce the risk of errors, accidents, and other negative outcomes. In a manufacturing setting, situational awareness can help human workers and robots to coordinate their actions effectively, ensuring that tasks are completed efficiently and safely. Similarly, in an autonomous vehicle, situational awareness can help the vehicle to detect and respond to changing road conditions, as well as the behaviour of other drivers and pedestrians.
This Research Topic focuses on exploring and improving the interaction between humans and machines while they work together effectively in complex, dynamic environments. More specifically, the goal is to develop and implement innovative technologies and strategies that enable effective collaboration between humans and intelligent machines/robots, as well as other advanced machines, with a focus on enhancing safety, efficiency, and performance.
The scope encompasses a broad range of topics. Specific themes that contributors are encouraged to address include, but are not limited to:
• Advances in sensors and monitoring systems for enhancing situational awareness in human-machine collaborative tasks
• The role of machine learning and predictive analytics in improving safety and efficiency in human-machine collaborative tasks
• Human factors and cognitive analysis in human-machine collaborative tasks
• Ergonomics and design considerations for interfaces and tools in human-machine collaborative tasks
• Augmented reality and virtual reality interfaces for enhancing situational awareness and coordination in human-machine collaborative tasks
• Collaborative awareness in cyber-physical systems and systems of systems
• Failure detection/prediction and warnings for disaster management in human-machine collaborative tasks
• Collaborative robots and their impact on situational awareness in manufacturing and healthcare
• Intelligent driver monitoring systems in autonomous driving
• AR explainable content in human-machine interfaces for increasing user's skills and safety
Contributors are invited to submit original research articles and perspective papers that address these themes and advance our understanding of situational awareness in human-machine collaborative tasks. We welcome contributions from researchers in a range of disciplines, including robotics, computer science, psychology, human factors, and engineering. Manuscripts should present novel research findings or perspectives and should contribute to the advancement of the field of situational awareness in human-machine collaborative tasks.
Keywords:
User’s situational awareness, Collaborative awareness in CPSoSs, Safety analysis methods in collaborative tasks, Human-machine interactive systems, User’s state monitoring, Ergonomics and cognitive task load, User’s productivity, skills and safety
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
Situational awareness is a critical component of effective collaboration between humans and machines in a variety of contexts, ranging from manufacturing and transportation, to healthcare and emergency response. Situational awareness refers to the ability of individuals and teams to understand and interpret the current state of a particular system or environment, including factors such as the location and status of equipment, the behaviour of other team members, and the overall context and objectives of the task at hand. In the context of human-machine collaborative tasks, situational awareness is particularly important, as it can help to reduce the risk of errors, accidents, and other negative outcomes. In a manufacturing setting, situational awareness can help human workers and robots to coordinate their actions effectively, ensuring that tasks are completed efficiently and safely. Similarly, in an autonomous vehicle, situational awareness can help the vehicle to detect and respond to changing road conditions, as well as the behaviour of other drivers and pedestrians.
This Research Topic focuses on exploring and improving the interaction between humans and machines while they work together effectively in complex, dynamic environments. More specifically, the goal is to develop and implement innovative technologies and strategies that enable effective collaboration between humans and intelligent machines/robots, as well as other advanced machines, with a focus on enhancing safety, efficiency, and performance.
The scope encompasses a broad range of topics. Specific themes that contributors are encouraged to address include, but are not limited to:
• Advances in sensors and monitoring systems for enhancing situational awareness in human-machine collaborative tasks
• The role of machine learning and predictive analytics in improving safety and efficiency in human-machine collaborative tasks
• Human factors and cognitive analysis in human-machine collaborative tasks
• Ergonomics and design considerations for interfaces and tools in human-machine collaborative tasks
• Augmented reality and virtual reality interfaces for enhancing situational awareness and coordination in human-machine collaborative tasks
• Collaborative awareness in cyber-physical systems and systems of systems
• Failure detection/prediction and warnings for disaster management in human-machine collaborative tasks
• Collaborative robots and their impact on situational awareness in manufacturing and healthcare
• Intelligent driver monitoring systems in autonomous driving
• AR explainable content in human-machine interfaces for increasing user's skills and safety
Contributors are invited to submit original research articles and perspective papers that address these themes and advance our understanding of situational awareness in human-machine collaborative tasks. We welcome contributions from researchers in a range of disciplines, including robotics, computer science, psychology, human factors, and engineering. Manuscripts should present novel research findings or perspectives and should contribute to the advancement of the field of situational awareness in human-machine collaborative tasks.
Keywords:
User’s situational awareness, Collaborative awareness in CPSoSs, Safety analysis methods in collaborative tasks, Human-machine interactive systems, User’s state monitoring, Ergonomics and cognitive task load, User’s productivity, skills and safety
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.