Sensor Fusion Architecture Validator
Validate sensor fusion architectures and generate ADAS test matrices
What You Can Do
You can submit proposed sensor fusion system architectures for structured validation that catches timing violations, coordinate frame mismatches, and redundancy gaps early in the design phase. Claude analyzes sensor interaction patterns, identifies data consistency requirements across cameras, LiDAR, radar, and inertial sensors, then generates detailed test matrices covering multi-sensor fallback modes and operational design domain constraints—accelerating pre-development reviews where architectural fixes are most cost-effective.
Features
Systematically evaluates sensor fusion topology, data flow paths, and integration points for design flaws
Identifies timing synchronization constraints, latency mismatches, and coordinate frame transformation complexities
Assesses sensor redundancy strategies and generates fallback scenarios for safety-critical functions
Creates comprehensive test cases covering multi-sensor combinations, sensor degradation modes, and ODD edge cases
Detects synchronization violations between asynchronous sensor inputs with different update rates
Recommends sensor calibration procedures and validation checkpoints for architecture implementation
Documents sensor failure modes, cross-dependencies, and system-level impact assessment
Example Output
Example 1: Architecture Review Output
Sensor Fusion Architecture: Camera + LiDAR + Radar Redundancy
✓ TIMING CONSTRAINTS
- Camera frame rate: 30 Hz (33ms latency acceptable)
- LiDAR scan rate: 10 Hz (100ms latency constraint)
- Radar update: 20 Hz (50ms max acceptable)
- Fusion cycle: 10 Hz, requires 300ms buffering strategy
⚠ IDENTIFIED ISSUES
- Coordinate frame transformation chain requires 4 rotation matrices (high complexity)
- LiDAR-camera misalignment tolerance: ±0.5° in pitch (calibration critical)
- Radar velocity measurement conflicts with camera optical flow in fog (fallback required)
Example 2: Test Matrix Excerpt
| Test Case | Sensor Configuration | Failure Mode | Expected Behavior | Pass Criteria |
|-----------|----------------------|--------------|-------------------|---------------|
| T-001 | All sensors nominal | Baseline | Full fusion output | <10cm error |
| T-012 | Camera fail | Vision loss | LiDAR+Radar hybrid | <50cm error, alert |
| T-024 | Rad vel conflict | Fog, rain | Optical flow override | <100ms switchover |
Example 3: Redundancy Gap Report
CRITICAL GAPS IDENTIFIED
- No fallback for LiDAR-only intersection detection (single point of failure)
- Recommendation: Add rule-based radar-camera crossing confirmation
- Test gap: No scenario covers simultaneous camera+LiDAR degradation
What's Included
- SKILL.md: Sensor fusion validation instruction framework
- Architecture Review Checklist: 25-point validation template covering topology, data consistency, timing, redundancy, and safety constraints
- Test Matrix Template: Structured test case generator with sensor combination matrix, failure mode scenarios, and acceptance criteria
- Coordinate Frame Worksheet: Transformation chain mapper identifying rotation matrix complexity and calibration requirements
- Timing Constraint Tracker: Synchronization violation detector and latency budget calculator for multi-rate sensors
Who It's For
- ADAS/Autonomous Vehicle Engineers — Validating sensor fusion system designs before prototype development
- Systems Architects — Reviewing multi-sensor integration topologies and redundancy strategies
- Safety Engineers — Assessing sensor fusion safety requirements and failure mode impacts
- Test Engineers — Generating comprehensive test matrices for multi-sensor validation campaigns
- Calibration Teams — Planning sensor calibration procedures and validation checkpoints
Best For
- Architecture design review — Structured validation of sensor fusion system topologies during early design phases
- Data consistency analysis — Identifying timing, latency, and coordinate frame mismatches across heterogeneous sensors
- Redundancy strategy validation — Assessing fallback modes and sensor degradation scenarios for safety-critical functions
- Test planning — Generating comprehensive test matrices covering multi-sensor combinations and edge cases within ODD
- Pre-implementation risk assessment — Catching architectural flaws before costly prototype development
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