Call for papers
Autonomous systems, such as self-driving cars, robots, and unmanned aerial vehicles, demand rigorous quality assurance to ensure their safety, reliability, and performance in diverse and unpredictable environments.
Achieving robust quality assurance involves rigorous testing and analysis in the entire system life-cycle to identify potential failures and optimize system performance under various conditions. Predictive capabilities are equally necessary, leveraging advanced technologies like machine learning and simulation to anticipate failures and adapt systems accordingly. By continuously monitoring and simulating behaviors in virtual environments, such as those created by digital twin technology, autonomous systems can undergo thorough testing, reducing the risks and costs associated with physical trials.
Digital twins offer a powerful solution to these challenges by providing virtual replicas of physical systems. Engineers can conduct extensive development, testing, and validation activities by continuously obtaining real-time data from the physical entity and simulating the behavior and interactions of autonomous systems in a controlled digital environment. Digital twins enable the emulation of various scenarios, including rare or hazardous conditions that are difficult or inconvenient to replicate in the real world. This capability accelerates the verification process and allows iterative improvements based on simulated data before physical deployment. Additionally, digital twins facilitate continuous monitoring and performance optimization throughout the lifecycle of autonomous systems, ensuring ongoing compliance with safety standards and operational requirements.
Using digital twins in autonomous systems is crucial for ensuring quality assurance and enhancing predictive capabilities. This technology enables real-time monitoring and simulation of system behavior, providing a detailed virtual replica that facilitates advanced testing and the correction of potential faults before physical production. Digital twins also support optimization of design and operational processes, reducing development times and enhancing overall efficiency. Moreover, by analyzing large datasets and simulating complex scenarios, digital twins aid in developing advanced algorithms and predictive resource management, improving system resilience and adaptability.
However, several challenges emerge in realizing a digital twin. This process involves creating detailed models of the system and its environment, which must remain aligned with both the environment's dynamic and open nature and the hardware status of the physical twin. This poses a significant challenge, as both autonomous systems and their digital twins must continually adjust to changes to maintain accuracy and functionality. Runtime techniques are therefore required to verify and adapt both autonomous systems and their digital twins.
Despite the great interest in enhancing autonomous systems' quality assurance and prediction through digital twins, no common methodologies, model-based techniques, or formal aspects have been fully established.
ASQAP 2026 aims to provide a forum for sharing and discussing innovative contributions to both formal and practical approaches in the analysis and development of methodologies, including digital twins, for the quality assurance of autonomous systems.
Topics of interests include, but are not limited to:
- Formal approaches to verification and validation of autonomous, multi-agent, and collaborative systems
- Practical applicability of formal methods for verification
- Verification and validation techniques for functional and non-functional assessment and dependability
- Creation and maintaining a digital twin in a dynamic environment
- Digital and Physical Twin co-development
- Digital Twin-based predictive maintenance
- Techniques for runtime verification and adaptation of Digital Twins
- Modeling, design, and engineering of digital twins for autonomous systems
- Domain-specific languages for autonomous systems
- Automated model-driven development of digital twins
- Dynamic verification and testing of autonomous systems
- Autonomous system quality assessment
- Autonomous Systems behavior prediction through digital twins
- Dynamic reconfiguration and optimization through digital twins
- Tools for analysis, verification, and validation of autonomous systems
- Case studies and experience reports from academia and industry
