Safety Verification for Autonomous Systems
Verify autonomous system safety, failure modes, and control robustness end-to-end
What You Can Do
Systematically verify safety in autonomous systems by performing failure mode analysis, testing control robustness, and simulating sensor failures to identify critical hazards. Claude generates detailed FMEA matrices, robustness assessments, and safety case documentation that trace requirements to verification evidence—accelerating pre-deployment certification and reducing oversight risk.
Features
Create comprehensive failure trees and hazard severity/likelihood matrices aligned with aerospace and automotive standards
Analyze stability margins, sensor uncertainty bounds, and actuator constraints to validate autopilot and guidance algorithm resilience
Define detection latency, false-positive rates, and evasion strategies for individual and cascading sensor failures
Map system requirements to verification artifacts and identify gaps in proof
Simulate edge cases (GPS loss, communications blackout, environmental hazards) and verify safe recovery behaviors
Model cross-subsystem failure propagation and identify single-points-of-failure requiring redundancy
Generate evidence structure compliant with IEC 61508, DO-178C, or ISO 26262 standards
Generate test scenarios and acceptance criteria after autopilot or navigation updates
Example Output
FMEA Excerpt: GPS Loss in Guidance System
| Failure Mode | Severity | Likelihood | Detection | RPN | Mitigation |
|---|---|---|---|---|---|
| GPS signal loss | High | Medium | 8s latency | 72 | INS fallback + geofence |
| False heading correction | Critical | Low | 2s latency | 45 | Heading rate sanity check |
Control Robustness Summary
- Stability margin: 2.1× at cruise altitude (exceeds 1.5× requirement)
- Sensor uncertainty impact: Heading error +/- 3° within acceptable tracking tolerance
- Actuator saturation recovery: Safe from stall with 0.5s latency in surface command
Sensor Failure Scenario: Barometric Altimeter
- Detection: Vertical acceleration deviates >0.3g within 4s
- Recovery: Switch to radar altitude with 200ft floor enforcement
- Test case: Simulate +1000 ft bias, verify auto-recovery completes in <6s
What's Included
- SKILL.md: Complete workflow for multi-system safety verification
- FMEA template: Pre-formatted worksheets for guidance, navigation, and propulsion subsystems
- Control robustness checklist: Stability margin, sensor uncertainty, and actuator constraint verification steps
- Sensor failure scenario matrix: Detection latencies, false-positive rates, and recovery strategies
- Safety case template: Argument structure compliant with major standards (IEC 61508, DO-178C, ISO 26262)
- Hazard interaction analysis worksheet: Cross-subsystem failure propagation and redundancy mapping
- Regulatory evidence checklist: Pre-flight certification requirements by jurisdiction (FAA, EASA)
Who It's For
- Autonomous vehicle safety engineers — Verify collision avoidance, sensor fusion, and override safety nets before road testing
- Robotics control system designers — Validate guidance algorithms and motor controller robustness in industrial or consumer robots
- Guidance and navigation engineers — Certify autopilot and path-planning control laws for aircraft, spacecraft, or marine vessels
- Unmanned aircraft systems (UAS) specialists — Pre-deployment safety verification for drone autopilots and sense-and-avoid systems
- Mobile robot platform architects — Analyze failure modes and mitigation strategies across multi-robot coordination and obstacle avoidance stacks
Best For
- Pre-deployment safety verification — Verify new control algorithms and sensor configurations before live testing or certification
- Failure mode analysis — Deep-dive FMEA on sensor redundancy architecture, actuator coupling, or environmental edge cases
- Robustness validation — Test autopilot and guidance system resilience under sensor noise, communication delays, and environmental uncertainty
- Safety requirements documentation — Generate traceable evidence that ties control design to safety requirements and verification tests
- Algorithm regression testing — After updates to navigation or guidance laws, systematically verify no safety margins were eroded
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