OPERATIONAL

Edge Vision (ADAS)

Predictive Hazard Detection & Material Logic

The Perception Gap

Contemporary autonomous systems frequently encounter "texture confusion" in low-contrast environments, such as identifying a white vehicle against a bright, overcast sky. When standard neural networks rely solely on pixel-match patterns, small variations in lighting can lead to critical detection failure or "hallucinations".

The Trident Prime Engine (ADAS Horizon) utilizes the ISED Framework to bridge this gap. This technology—protected by U.S. Patent Application No. 63/940,736 and 63/983,021—implements Spectral Gradient Mapping and high-fidelity Spectral Structure Tensor analysis to achieve certainty where others have probability. The core logic is governed by the Spectral Consistency Index:

Governing_Interpretability: This "Glass Box" audit provides the physics-native logic required for industrial safety verification. The system operates on verifiable physical invariants to ensure zero-training bias and zero-ML failure modes.

Our proprietary multi-channel spectral decomposition logic handles extreme dynamic range, while Full Stokes Polarimetry recovers surface normals and material classification to achieve high rejection of environmental noise.

Safety is anchored by adversarial integrity auditing, which provides a statistical integrity check of the spectral data. If a sensor failure or sensor spam injection is detected within the temporal frame-buffer, the engine applies a spectral alignment correction, maintaining inference stability before the downstream control signals can be compromised.

Spectral Threat Detection — Live Scan

                            MODE: ACTIVE

                            TARGET: LOCKED

CONFIDENCE MATRIX

Pedestrian 100.0%

Vehicle 100.0%

Occlusion RESOLVED

System uses spectral invariant analysis to predict occluded trajectories with high-confidence accuracy.

SYSTEM LOG

[TRIDENT] Spectral scan active...
[ENGINE] Consistency check: Validated
[AUDIT] Sensor integrity secured.

Technical Verification | Spectral Invariance

Conventional LIDAR/Vision fusion fails when encountering "phantom" data — sensor noise or adversarial patterns that look like real objects (High-Confidence False Positives).

Trident (B27) applies a spectral invariant consistency check. As shown in the benchmark, the red "Phantom" noise patch has high intensity but low spectral coherence (random anisotropy). The Trident filter rejects this inconsistency, eliminating the false positive before it reaches the decision layer.

TEST_ID: ADAS_SPECTRAL_V5 SCENARIO: HALLUCINATION_INJECTION

Trident ADAS Spectral Invariant Benchmark

Automotive & Safety Value

Edge Vision provides physics-native perception that operates on verifiable physical invariants — zero training bias, zero ML failure modes:

L4/L5 Autonomous Driving — Guaranteed hazard detection in fog, rain, and glare conditions where neural networks hallucinate
Military ISR Platforms — Material classification of targets through Full Stokes Polarimetry, distinguishing metal from foliage at range
Agricultural Robotics — Crop health assessment via spectral gradient mapping, independent of lighting conditions

Integration Path: B27 feeds into B16-GEO to provide glare-resilient SLAM landmarks for navigation in degraded visual environments.