A3AIDRM: AI-Driven Road Management
About this Course
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.
Audience Profile
At Course Completion
• 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.
Outline
Course Outline
1. Introduction and Core Concepts
● Welcome and course objectives
● The big picture: How AI is revolutionizing road infrastructure management
● Predictive maintenance, resource optimization, and data-driven decisions
● Key terminology: AI, CV, edge computing, geospatial data
● Overview of AI-driven road management workflow (data collection to actionable insights)
● Activity: Discuss local DoT challenges and AI opportunities
2. Essential Technologies for AI Road Management
● Data Collection Methods:
○ Drones
○ LiDAR sensors
○ Dashcams and vehicle-mounted cameras
● Common File Formats:
○ GeoTIFF (georeferenced imagery)
○ LAS/LAZ (LiDAR point clouds)
○ GeoJSON (vector geospatial features)
● AI Models and Computer Vision:
○ YOLO (You Only Look Once):
■ Real-time object detection model
■ Outputs bounding boxes around potholes, cracks, faded lines, signage
■ Used for rapid road defect detection on live video streams
○ Semantic Segmentation:
■ Classifies each pixel into categories (e.g., road, crack, vegetation)
■ Helps in understanding road surface condition at a detailed level
○ Classification Models:
■ Categorize road types, damage types, sign categories
■ Useful for inventory systems and rule-based compliance
○ Bounding Boxes & Labels:
■ Standard way to annotate and train detection systems
■ Integral to training data pipelines and performance measurement
○ Model Selection Considerations:
■ CNNs vs. Transformers (efficiency vs. context-awareness)
● Edge Computing:
○ Devices like Nvidia Jetson and Coral TPU for on-vehicle inference
○ Real-time defect detection and geotagging at the source
● Geospatial Systems:
○ GIS (e.g., QGIS, ArcGIS)
○ OpenStreetMap and PostGIS for spatial data storage and queries
● Lab: Explore sample GeoTIFF and GeoJSON data in a GIS tool
3. Case Studies and Technology Applications
● Real-world examples: AI for pothole detection, crack assessment, and predictive analytics
● Examples: Urban pilots (e.g., San Jose, CA), rural deployments, crowdsourced data
● Technology deep dive: Object detection, semantic segmentation, and performance metrics (mAP, IoU)
● Activity: Analyze a case study and identify applicable technologies for your DoT
4. Backend Systems and Data Processing
● Backend Architecture:
○ Databases (PostgreSQL with PostGIS extension)
○ Data pipelines for image ingestion, labeling, analysis
● Post-Processing Tasks:
○ Georeferencing imagery
○ Damage severity scoring
○ Prioritizing maintenance actions
● Advanced Analytics with LLMs (Large Language Models):
○ LLMs as Assistants in Road Management:
■ Generate human-readable maintenance reports from AI detection results
■ Suggest repair strategies based on past outcomes or standards
■ Summarize incident patterns and highlight high-risk areas
■ Convert raw AI metrics into regulatory or funding-compliant language
■ Help draft procurement language or RFPs with data-based justification
○ Integration of ChatGPT-style models via API or internal LLMs
● Integration with Existing DoT Systems:
○ GIS dashboards
○ Work order systems
○ Asset lifecycle management tools
● Lab: Simulate a data pipeline from raw imagery to GIS output
● Activity: Draft functional requirements for a DoT’s backend system
5. Performance Requirements and Validation
● Defining key performance indicators (KPIs): Precision, recall, F1-score, IoU
● Setting realistic targets for defect detection (e.g., potholes, cracks)
● Validation strategies: Unseen test data, phased testing, pilot programs
● Lab: Calculate performance metrics using sample AI outputs
● Activity: Draft performance targets for two defect types (e.g., potholes, alligator cracking)
6. Advanced Data Requirements and Governance
● Data Types:
○ Imagery (GeoTIFF)
○ LiDAR (LAS/LAZ)
○ Vector data (GeoJSON)
● Data Quality Topics:
○ Annotation best practices
○ CRS selection and harmonization
○ Scalable storage and retrieval
● Governance Considerations:
○ Data ownership and chain of custody
○ Encryption and role-based access control
○ API standards for integration with internal and external systems
● Lab: Preprocess sample imagery and assign CRS using GDAL/Rasterio
● Activity: Define data ownership and security requirements for a DoT
7. Non-Functional Requirements (NFRs)
● Usability: Intuitive interfaces, minimal training needs
● Reliability: Uptime (e.g., 99.5%), fault tolerance
● Scalability: Handling increased data volumes and users
● Security: Protecting data and systems from threats
● Maintainability: Updates, support, and documentation
● Activity: Prioritize top NFRs for your DoT and draft one measurable requirement per NFR
8. Ensuring Fair and Reliable AI
● Understanding performance gaps: Urban vs. rural, weather conditions, pavement types
● Strategies for robustness: Diverse training data, continuous monitoring
● Promoting fairness: Equitable resource allocation, transparency
● Lab: Analyze AI outputs for performance gaps across road conditions
● Activity: Draft a requirement for vendors to address performance gaps
9. Crafting Specifications and Workshop
● Structuring a specification document: Scope, functional/non-functional requirements, performance, data governance
● Best practices: Use “SHALL” for mandatory, S.M.A.R.T. criteria, focus on outcomes
● Evaluation criteria: Technical feasibility, fairness, total cost of ownership
● Workshop: Envision AI in your DoT
○ Participants draft a mini-specification for an AI road management system
○ Group discussion: Share and refine specifications
● Activity: Identify evaluation criteria and weight them for your DoT
10. Final Summary and Next Steps
● Recap of key takeaways: AI/CV fundamentals, data quality, performance, fairness
● Action plan: Applying course concepts to your DoT
● Resources for further learning and vendor engagement
● Q&A and course evaluation
Resources and Support
• Reference materials and guides
• Online resources and communities
• Support channels and contact information
• Further learning paths
• Access to workshop materials
Final Q&A and Closing
• Addressing remaining questions
• Workshop evaluation collection
• Contact information exchange
• Next steps and follow-up plan
• Future workshop opportunities
Prerequisites
• While the course is designed to be accessible for participants of varying experience levels, having some technical familiarity will enhance the learning experience and enable attendees to engage more effectively with hands-on activities and advanced topics