NAVIQ Decision Benchmarks: Evaluation Framework for Autonomous Machine Intelligence

NAVIQ Decision Benchmarks: Evaluation Framework for Autonomous Machine Intelligence

Overview

This document describes the benchmark suite and evaluation methodology used to assess NAVIQ decision intelligence systems for autonomous machines. The framework provides standardized metrics for perception, planning, decision-making, and control performance.

Benchmark Categories

Perception Accuracy

Measures the correctness of sensor interpretation across varying environmental conditions. Metrics include object detection precision/recall, segmentation accuracy, and state estimation error.

Decision Latency

Evaluates the time from sensor input to action output, broken down by subsystem (perception → understanding → decision → planning → control). Critical for real-time autonomous operation.

Planning Quality

Assesses the optimality and feasibility of generated plans compared to ground truth or expert demonstrations. Includes path length, energy efficiency, and constraint satisfaction metrics.

Control Stability

Evaluates the smoothness and accuracy of executed trajectories, measuring tracking error, control effort, and oscillation tendencies.

Safety Metrics

Quantifies risk exposure through proximity to obstacles, adherence to safety constraints, and failure mode analysis under various fault conditions.

Benchmark Environments

Simulation Benchmarks

Controlled environments in Gazebo and Unity with repeatable scenarios and configurable difficulty levels. Enables rapid iteration and parameter sweeps.

Real-World Test Tracks

Instrumented facilities with ground truth tracking via motion capture and sensor networks. Provides realistic lighting, weather, and surface condition variations.

Field Deployment Trials

Long-term testing in operational environments with actual work tasks and unpredictable conditions. Measures robustness and adaptation capabilities.

Evaluation Protocol

Each benchmark scenario includes:

1. Initialization procedures and preconditions
2. Execution protocol with defined success criteria
3. Measurement procedures for each metric category
4. Post-condition validation and cleanup
5. Statistical analysis requirements for significance testing

Key Findings from Recent Evaluations

Domain Adaptation

Systems trained with domain randomization show 2-3x improvement in real-world transfer compared to simulation-only training.

Planning-Realtime Tradeoff

Hierarchical planning enables real-time replanning while maintaining solution quality within 15% of offline optima.

Safety Guarantees

Formal verification of decision bounds provides provable safety guarantees under specified assumptions.

Computational Efficiency

Optimized inference pipelines achieve 30ms end-to-end latency on embedded hardware for simple navigation tasks.

Open Challenges

Long-Horizon Planning

Maintaining computational tractability for planning horizons exceeding 30 minutes in dynamic environments.

Multi-Agent Coordination

Extending benchmarks to evaluate collaborative behaviors and conflict resolution in shared workspaces.

Edge Case Handling

Systematic evaluation of rare but critical failure scenarios that may not appear in standard benchmark suites.

Reproducibility

All benchmark scenarios, execution scripts, and analysis tools are available in the associated repository. Reference implementations include baseline algorithms for comparison.