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Autonomous Systems

Decision intelligence for autonomous machines — perception, state understanding, decision, planning, and closed-loop control. Powered by NAVIQ.

The Problem

Autonomous machine development requires expensive, fragmented decision stacks that don't transfer between domains.

Current Workflow

1

Fragmented stacks

Each capability from different vendor, poorly integrated

2

No transfer learning

Models trained per-domain, no reuse across applications

3

Safety certification gap

No standard safety architecture across components

4

Simulation disconnect

Sim-to-real transfer is manual and unreliable

OPTINX Approach

1

Sensor integration

NAVIQ ingests camera, LiDAR, radar, IMU, and GPS streams

2

Perception pipeline

Object detection, segmentation, tracking, scene understanding

3

State estimation

Scene semantics, intent prediction, risk assessment

4

Decision & planning

Policy evaluation, trajectory generation, constraint satisfaction

5

Closed-loop control

Continuous feedback, deviation detection, adaptive replanning

Architecture

1

Sensors

CamerasLiDARRadarIMUGPSUltrasonic
2

NAVIQ Decision Stack

Perception fusionState estimationPolicy evaluationTrajectory planningClosed-loop control
3

QUACK Runtime

Real-time orchestrationSafety monitoringFallback management
4

Nextmos Memory

Operational experienceIncident patternsEnvironmental models

Governance & Security

Human Approval Points

Safety-critical maneuvers require human supervisor acknowledgment
New deployment environments require safety case review
Model updates require simulation validation before deployment

Security Considerations

On-vehicle processing for safety-critical latency
Encrypted communication for remote monitoring
No sensor data shipped to cloud without explicit consent
Full decision audit trail for incident reconstruction

Potential Outcomes

Potential outcomes based on architecture and approach. Actual results depend on integration depth and data quality.

Development Velocity

Shared infrastructure reduces per-application engineering

Potential

Safety

Redundant safety monitors and fallback behaviors

Potential

Transfer Learning

Knowledge transfers across applications and environments

Potential

Certification

Standard safety architecture supports regulatory approval

Potential
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