Source record

AAR and ASLRRA Comments, Docket No. FRA-2024-0032 (describing the verification workflow, the 24-hour window for one-class-drop defects, and immediate slow orders for two-class drop

Method: Literature

AAR and ASLRRA Comments, Docket No. FRA-2024-0032 (describing the verification workflow, the 24-hour window for one-class-drop defects, and immediate slow orders for two-class drops under the test program terms).

A Framework for Trustworthy Deployment: Governance, Human-Automation Teaming, and a Phased Implementation Roadmap for AI in Track Base Management

When the Model Is Wrong: Risks, Hazards, and Failure Modes of AI in Safety-Critical Track Inspection

The Economics of Knowing: Predictive Maintenance, Degradation Forecasting, and the Asset Value Case for Substructure AI

A Framework for Trustworthy Deployment: Governance, Human-Automation Teaming, and a Phased Implementation Roadmap for AI in Track Base Management

When the Model Is Wrong: Risks, Hazards, and Failure Modes of AI in Safety-Critical Track Inspection

The Economics of Knowing: Predictive Maintenance, Degradation Forecasting, and the Asset Value Case for Substructure AI

A Framework for Trustworthy Deployment: Governance, Human-Automation Teaming, and a Phased Implementation Roadmap for AI in Track Base Management

When the Model Is Wrong: Risks, Hazards, and Failure Modes of AI in Safety-Critical Track Inspection

The Economics of Knowing: Predictive Maintenance, Degradation Forecasting, and the Asset Value Case for Substructure AI

A Framework for Trustworthy Deployment: Governance, Human-Automation Teaming, and a Phased Implementation Roadmap for AI in Track Base Management

When the Model Is Wrong: Risks, Hazards, and Failure Modes of AI in Safety-Critical Track Inspection

The Economics of Knowing: Predictive Maintenance, Degradation Forecasting, and the Asset Value Case for Substructure AI