InspectGuard AI
Governed verification and validation platform for transportation AI systems, supporting oversight of perception, localization, planning, and control functions to demonstrate safety and regulatory readiness.
The Problem
“InspectGuard AI for governed verification and validation of transportation AI systems”
Organizations face these key challenges:
Safety evidence is distributed across logs, simulation outputs, requirements systems, and narrative reports
Regulators and executives struggle to interpret highly technical validation artifacts
Disengagement reports are inconsistent and difficult to compare across programs
Scenario coverage gaps are hard to identify before deployment
Benchmarking for near-field perception, prediction, and action-aware scene understanding is inconsistent
Engineering validation and compliance documentation are often separate and manually reconciled
State and federal policy expectations are fragmented and evolve over time
Root-cause analysis for failures across perception, localization, planning, and control is slow
Impact When Solved
The Shift
Human Does
- •Collect validation evidence from simulation, test-track, field logs, requirements records, and issue trackers
- •Manually trace requirements to scenarios, tests, model versions, and observed results
- •Review perception, localization, planning, and control outputs to identify failures and regressions
- •Assemble safety case materials and readiness summaries for internal and regulatory review
Automation
- •Limited or no AI support in the legacy workflow
- •Basic tool outputs provide raw test results and logs
- •Separate dashboards show isolated metrics without governed cross-artifact reasoning
Human Does
- •Approve validation plans, release readiness decisions, and safety case conclusions
- •Review prioritized anomalies, residual risks, and recommended follow-up actions
- •Resolve exceptions where evidence is incomplete, conflicting, or outside policy thresholds
AI Handles
- •Ingest and organize validation artifacts into a governed evidence trail across simulation, track, and field sources
- •Classify scenarios, map requirements to test artifacts, and measure coverage across perception, localization, planning, and control
- •Detect anomalies, summarize regressions and failures, and prioritize validation gaps by risk
- •Orchestrate validation workflow steps, generate evidence packets, and monitor readiness against approval criteria
Operating Intelligence
How InspectGuard AI runs once it is live
AI runs the first three steps autonomously.
Humans own every decision.
The system gets smarter each cycle.
Who is in control at each step
Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.
Step 1
Assemble Context
Step 2
Analyze
Step 3
Recommend
Step 4
Human Decision
Step 5
Execute
Step 6
Feedback
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.
The Loop
6 steps
Assemble Context
Combine the relevant records, signals, and constraints.
Analyze
Evaluate options, risk, and likely outcomes.
Recommend
Present a ranked recommendation with supporting rationale.
Human Decision
A human accepts, edits, or rejects the recommendation.
Authority gates · 1
The system must not approve a release-readiness decision or safety case conclusion without sign-off from the designated safety or validation authority. [S4][S8][S12]
Why this step is human
The decision carries real-world consequences that require professional judgment and accountability.
Execute
Carry out the approved action in the operating workflow.
Feedback
Outcome data improves future recommendations.
1 operating angles mapped
Operational Depth
Technologies
Technologies commonly used in InspectGuard AI implementations:
Key Players
Companies actively working on InspectGuard AI solutions:
Real-World Use Cases
Planned browser fingerprinting to reduce unnecessary proof-of-work challenges
The site plans to check whether a visitor looks like a real browser, so trusted users may skip the puzzle while suspicious automated tools still get challenged.
Explainable safety argumentation for automated vehicle deployment
Create a structured, human-understandable case for why an automated driving system is safe enough to be deployed, especially in messy real-world conditions.
Benchmarking and evaluation pipeline for near-field 3D perception and prediction
Create a fair test so engineers can measure how well autonomous vehicles understand and predict what is happening very close to them.
Federal AV safety oversight framework for commercial ADS deployment
The government is building a national rulebook to let self-driving vehicles operate commercially while making sure they are safe.
Virtual validation tool chain for autonomous vehicles
A software pipeline tests self-driving vehicle behavior in a computer-generated world before trying it on real roads.