It is anticipated that real-world autonomous systems, such as AI-enabled robotic systems and intelligent transportation systems, will increasingly be deployed in real-world environments within the next few decades, precluding mundane human work, providing new services, and facilitating smarter and more flexible infrastructure in workplaces, public spaces, and homes. For such systems to be safe and secure, especially around people, it is important that they are explainable, predictable, and can deal with a real world that is constantly changing and only partially observable.
This book, Safety-Aware Autonomous Systems - Preparing Robots for Life in the Real World, investigates ways for autonomous systems to deal with dynamic and changing environments consistently and safely. The need for sound uncertainty management and the explicit uncertainty quantification required to provide probabilistic safety guarantees is explored, as is the runtime monitoring necessary to ensure safety in new situations. The realization of well-grounded prediction and classification uncertainty and the capacity to deal with unknown unknowns using anomaly detection are also interrogated. The degree of safety required can make it very challenging to reach useful levels of efficiency and effectiveness in safety-critical applications. To this end, a holistic perspective on agent motion in complex and dynamic environments is investigated. The book also discusses the leveraging of the synergies in well-founded formalized interactions, and the integration between learning, reasoning, and interaction.
Demonstrating efficient, effective, and safe capabilities for autonomous systems in safety-critical situations, the book will be of interest to all those working in the field.
