Autonomous Systems and Engineering Oversight for Texas Professional Engineers:
 

AI-Enabled Decision-Making, Human-in-the-Loop Controls, Safety Assurance, Risk Management, Regulatory Compliance, and Professional Responsibility

Course Description:
 

Autonomous systems are rapidly transforming engineering practice across transportation networks, utility infrastructure, industrial facilities, public works systems, construction operations, asset management programs, and critical infrastructure environments. Artificial intelligence, machine learning, robotics, autonomous inspection technologies, predictive analytics, digital twins, advanced sensing systems, and automated decision-support platforms are increasingly being used to support engineering activities that historically relied upon direct human observation and analysis. While these technologies offer significant opportunities to improve efficiency, enhance situational awareness, strengthen operational reliability, and support more informed decision-making, they also introduce important challenges involving oversight, accountability, safety, cybersecurity, ethics, and professional responsibility.

This course examines the growing role of autonomous systems within engineering environments and provides a comprehensive framework for understanding how engineers can responsibly govern, evaluate, and oversee these technologies. Participants will explore the technical foundations of autonomous systems, including artificial intelligence, machine learning, sensor fusion, digital infrastructure, autonomous inspection platforms, predictive analytics, and cyber-physical systems. The course explains how autonomous technologies differ from traditional automation and examines the opportunities and risks associated with increasing levels of machine-assisted decision-making.

The course places particular emphasis on the engineer's continuing responsibility to exercise independent professional judgment when autonomous technologies influence engineering decisions. Participants will examine human-in-the-loop, human-on-the-loop, and human-out-of-the-loop operational models and evaluate how appropriate oversight structures can be established to maintain accountability while benefiting from autonomous capabilities. The course also explores the importance of verification, validation, reliability assessment, performance monitoring, change management, and lifecycle governance for autonomous systems operating within safety-critical environments.

Safety assurance and risk management principles are examined in detail, including hazard identification, failure mode analysis, defense-in-depth strategies, fail-safe design, resilience engineering, emergency response planning, and operational risk evaluation. Participants will learn how autonomous systems can introduce both traditional engineering risks and new challenges associated with software, data quality, machine learning limitations, communications infrastructure, and system complexity. The course also addresses the critical relationship between cybersecurity and autonomous infrastructure, including operational technology security, cyber-physical system protection, sensor integrity, communications reliability, incident response planning, and critical infrastructure resilience.

Ethics, liability, and professional responsibility are integrated throughout the course. Participants will examine how the Texas Engineering Practice Act, professional engineering ethics principles, and emerging artificial intelligence governance frameworks apply when autonomous technologies contribute to engineering analysis, operational decisions, infrastructure management, and safety-related activities. The course discusses accountability, transparency, explainability, bias, documentation requirements, responsible supervision and control, and the continuing obligation of engineers to protect public health, safety, and welfare.

To reinforce these concepts, the course includes three detailed case studies based on realistic engineering scenarios involving autonomous technologies. The first case study examines an autonomous drone bridge inspection program in which machine learning and computer vision technologies assist infrastructure inspections but reveal important lessons regarding validation, oversight, and engineering accountability. The second case study analyzes an AI-controlled industrial facility monitoring system that successfully supports predictive maintenance activities but encounters challenges involving data quality, model limitations, automation bias, and reliability engineering governance. The third case study explores the use of an autonomous utility grid management platform during a severe Texas weather emergency and demonstrates the importance of engineering judgment when autonomous systems operate under conditions characterized by uncertainty and rapidly changing circumstances.

Each case study includes a Learning Activity that challenges participants to evaluate governance decisions, identify oversight weaknesses, assess risks, and develop engineering controls appropriate for autonomous system deployment. These activities encourage participants to apply course concepts to realistic operational environments and strengthen their ability to evaluate autonomous technologies from both technical and professional responsibility perspectives.

Throughout the course, Professional Judgment Alerts highlight critical situations where engineers must exercise heightened oversight and independent evaluation when relying upon autonomous systems. These alerts reinforce the central theme of the course: autonomous technologies may enhance engineering practice, but they do not replace the engineer's obligation to apply professional judgment, maintain responsible control, and protect public welfare.

Upon completion of this course, participants will possess a comprehensive understanding of autonomous systems and engineering oversight principles, including governance frameworks, risk management strategies, cybersecurity considerations, reliability assurance practices, ethical obligations, and professional responsibilities necessary to support the safe, effective, and responsible deployment of autonomous technologies across engineering disciplines.
 

Learning Objectives:
 

Upon completion of this course, participants will be able to:

1. Distinguish between traditional automation, autonomous systems, and AI-enabled decision-support technologies used in engineering practice.

2. Evaluate the capabilities, limitations, and operational risks associated with autonomous systems deployed within infrastructure, utility, industrial, transportation, and asset management environments.

3. Apply human-in-the-loop, human-on-the-loop, and engineering oversight principles to maintain accountability and professional control over autonomous system operations.

4. Assess verification, validation, reliability, and performance monitoring requirements necessary to support trustworthy autonomous system deployment.

5. Identify safety, operational, cybersecurity, and resilience risks associated with autonomous and AI-enabled engineering systems.

6. Analyze autonomous system failure scenarios and develop appropriate risk management, mitigation, and governance strategies.

7. Evaluate cybersecurity threats affecting autonomous infrastructure, operational technology environments, and cyber-physical systems.

8. Apply the Texas Engineering Practice Act and professional engineering ethics principles to autonomous system implementation and oversight.

9. Determine appropriate levels of engineering review, documentation, accountability, and decision authority when autonomous technologies influence engineering decisions.

10. Develop engineering oversight frameworks that integrate professional judgment, risk management, cybersecurity, safety assurance, and regulatory compliance into the governance of autonomous systems.