2025 IEEE Engineering Reliable Autonomous Systems ( ERAS)

Relevant Topics

General Understanding of Autonomy

Understanding the issues of autonomy, especially under the excess uncertainty of complex deployments (uncertainty and complexity permeate autonomous system design and operation - reliability requires that we address them)

Development of scientific foundations for autonomy that can drive the development of reliable systems

Application of the scientific method to autonomous systems experimentation and evaluation

Reproducibility, replicability, and generalizability of autonomous systems experiments

Research challenges and roadmaps for future development

Specification and User Needs

Defining, ensuring, and assessing system properties (safety, security, functionality, reliability, dependability, trustworthiness, …)

Stakeholder communication, expressing user needs and experiences, designer decisions and assumptions, test and evaluation results, operational expectations

Specification of requirements

System purpose, goals, and expectations as expressed by designers, testers, certification agents, users, customers, bystanders, etc.

Establishing trust and understanding of autonomous system behavior

Design and Systems Engineering for Reliability

System engineering and design principles (including reliable / dependable robot control architectures, systems that incorporate a range of artificial intelligence capabilities, and assurance for design as well as design for assurance)

Fault handling (including prediction, detection, isolation, identification, response, recovery, prevention, tolerance, removal) and runtime methods for recognition and recovery (monitoring, diagnosis, reonfigurability, verification, assurance) - runtime systems that provide resilience and dynamic functionality - autonomy that exists within the system to handle cases that fall outside nominal bounds)

Understanding and analysing trade-offs in system development (e.g. efficiency vs. transparency/verifiability/explainability; design-time vs. run-time; "Hoping for the best" vs. "Expecting the worst")

Assessing and Communicating Reliability

Test and evaluation techniques, principles, methods, tools, etc.

Verification and validation of autonomy (including techniques, processes, principles, methods, tools, verification of decision making, handling of unknown unknowns, uncertainty, and complexity)

Verification and validation of test and evaluation techniques, tools, etc.

Assurance and evidence (including types of evidence, confidence, evidence gathering, specific assurance cases for autonomous systems, structuring and design principles for assurance case development / integration of evidence into assurance)

Measurement and metrics

Mapping from evaluation results to operational performance

Context and impact of verification and evaluation on reliability

Runtime evaluation and assessment

Perceived and actual reliability

Reliability in Context

Licensure, regulatory approval, and certification

Standards and industry benchmarks

Legal and ethical considerations (including insurance and liability as well as government and public service considerations)

Industrial case studies and considerations

Life cycle considerations and methodologies (impact of reliability concerns on need identification, specification, design, development, evaluation and assessment of trustworthiness, operation, decommission)

Case studies (focused in specific application domains (e.g. healthcare diagnosis and intervention / autonomous driving / domestic robotics), specific environmental domains (e.g. space / maritime / volcanos / office buildings) and specific research domains (e.g. controls / perception))

Problem sets, reliability benchmarks and competitions