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Course Overview
This course equips transportation professionals with the knowledge and skills to transform road asset management using artificial intelligence (AI) and computer vision (CV). Participants will explore how AI-driven systems enable predictive maintenance, optimize resource allocation, and support data-driven decisions for road networks. Through a blend of lectures, case studies, and hands-on labs, attendees will learn to define functional and non-functional requirements, select appropriate AI models, and craft specifications for AI-based road management solutions. The course is vendor-neutral, focusing on universal principles and technologies applicable to any DoT, ensuring participants can envision and implement AI solutions tailored to their organization's needs.
Who Should Attend
This course is recommended for Project Managers, Architects, Developers, and Data Acquisition Specialists
Course Objectives
- Understand core AI and CV concepts for road inspection and asset management.
- Identify the roles of data, human oversight, and technology in AI-driven systems.
- Define essential functional and non-functional requirements for AI road management solutions.
- Establish meaningful performance metrics and data requirements to ensure system reliability.
- Develop a specification framework to describe and procure an AI-driven road management plan.
- Evaluate AI solutions for fairness, robustness, and integration with existing DoT systems.
Course Outline
- Introduction and Core Concepts
- Essential Technologies for AI Road Management
- Case Studies and Technology Applications
- Backend Systems and Data Processing
- Performance Requirements and Validation
- Advanced Data Requirements and Governance
- Non-Functional Requirements (NFRs)
- Ensuring Fair and Reliable AI
- Crafting Specifications and Workshop
- Course Summary