LATTICE for Autonomous Systems
From Intent to Action with Mathematical Proof
The Challenge
Autonomous systems—vehicles, drones, robots—must make split-second decisions with life-or-death consequences. Every action must be safe, legal, and ethically sound. Traditional AI offers no guarantees, and when a Tesla crashes or a drone malfunctions, the black box provides no answers.
The LATTICE Solution
Key Components
🛡️ Provable Safety
Safe(Action) ⊆ SafetyEnvelope
LQL encodes safety constraints mathematically, making unsafe actions impossible by construction.
⚡ Real-Time Processing
Decision(t) < 10ms
LEF particles process sensor data in parallel, guaranteeing sub-10ms decision cycles for critical maneuvers.
📝 Complete Traceability
∀Action: ∃Proof
Every decision generates a mathematical proof trail, enabling perfect accident reconstruction.
Autonomous Vehicle Architecture
PERCEPTION LAYER ├─→ LIDAR: 3D point cloud @ 10Hz ├─→ Cameras: 8x feeds @ 60fps ├─→ Radar: Velocity vectors └─→ Ultrasonic: Proximity alerts COGNITION LAYER (AIOS) ├─→ CNS: Sensor fusion & routing ├─→ COE: Situation assessment └─→ Path planning & prediction DECISION LAYER (LQL) ├─→ Safety constraints ├─→ Traffic law compliance ├─→ Ethical decision framework └─→ Optimization objectives EXECUTION LAYER (LEF) ├─→ Motor control particles ├─→ Steering particles ├─→ Braking particles └─→ Signal particles PROOF GENERATION └─→ Real-time proof of safety
Safety Guarantees
🚦 Traffic Law Compliance
- Speed limits mathematically enforced
- Traffic signals respected by design
- Lane discipline guaranteed
- Right-of-way rules encoded
⚠️ Collision Avoidance
- Minimum safe distance maintained
- Emergency braking within physics
- Predictive path planning
- Multi-agent coordination
Case Study: Autonomous Fleet Deployment
A logistics company deployed LATTICE-powered autonomous trucks for highway freight transport. After 1 million miles:
Accidents
Law Compliance
Decision Time
Fuel Savings
Ethical Decision Framework
LATTICE handles ethical dilemmas through mathematical optimization with clear priorities:
query EmergencyManeuver {
scenario: CollisionScenario {
obstacles: [Pedestrian, Vehicle, Barrier]
velocities: [0, 35mph, 0]
distances: [15m, 20m, 10m]
}
// Compute all feasible actions
actions: LEF.compute {
brake: MaxBraking(current_speed)
swerveLeft: Swerve(-30deg, physics)
swerveRight: Swerve(30deg, physics)
}
// Apply ethical constraints
ethical: LQL.optimize {
minimize: ExpectedHarm(actions)
subject_to: [
Legal(actions),
Feasible(actions, physics),
ProtectOccupants(actions)
]
}
// Execute with proof
execute: {
action: ethical.optimal
proof: generateSafetyProof(action)
log: recordDecision(scenario, action, proof)
}
}Applications Beyond Vehicles
🚁 Delivery Drones
Airspace compliance, weather adaptation, safe package delivery
🏭 Industrial Robots
Human-robot collaboration, safety zones, predictable behavior
🚢 Maritime Autonomy
Navigation rules, collision avoidance, port operations
Certification & Compliance
LATTICE's mathematical proof system enables formal certification:
- ISO 26262: Automotive functional safety
- DO-178C: Airborne systems certification
- IEC 61508: Safety-critical systems
- SOTIF: Safety of the intended functionality
Every decision comes with a mathematical proof that can be submitted for regulatory approval and insurance validation.