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
“Governed AI verification and validation for autonomous transportation systems”
Organizations face these key challenges:
Manual test case creation misses rare but safety-critical traffic interactions
Scenario generation does not scale with expanding operational design domains
Validation data is fragmented across simulators, logs, issue trackers, and document systems
Safety evidence is difficult to normalize, compare, and reuse across releases
UL 4600-style safety case authoring is labor-intensive and inconsistent
Weak governance over model versions, test baselines, approval workflows, and evidence lineage
Hard to demonstrate that perception, localization, planning, and control changes remain within acceptable risk bounds
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 release readiness, safety case conclusions, or regulator-facing evidence packages without sign-off from the designated human governance authority [S2][S3].
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
Autonomous Vehicle Safety Case Authoring for UL 4600 Compliance
Build a structured evidence package that explains why a self-driving vehicle is safe enough to deploy.
Probabilistic traffic scenario generation for AV safety testing
Engineers use software to automatically invent many realistic traffic situations so an autonomous vehicle can be tested against tricky cases before going on the road.