Technical overview

Driver Monitoring System

Occupant cognition · hazard fusion · forensic ledger

A real-time cognitive–motor threat surface mapped from facial micro-dynamics, then collapsed into dual, non-commuting alert manifolds—fatigue versus visual escape from the forward field.

Canonical entry: https://dms.aher.dev/

Split-brain topology

Raw photons never leave the observation plane: inference stays on the local capture stratum. Only collapsed statistics—aspect ratios, pose cosines, iris-offset fields—cross the trust boundary on a versioned binary telemetry envelope (metrics_batch v1), keyed by session UUID and stamped with client monotonic time.

Upstream sits an async ASGI control plane with lifecycle-gated schema materialization: SQLAlchemy 2.0 declarative models, idempotent create_all bootstrap, and a write path that treats trips as first-class causal traces—not logfiles. Persistence is PostgreSQL over an async wire with negotiated TLS; offline / lab runs can snap to embedded SQLite without touching application code.

Perception substrate

Inference & policy

Data plane

Trips mirror session identity one-to-one: bounded route_json polylines, odometer from speed–time quadrature, monotonic alert counters, append-only alert ledger for post-hoc reconstruction—what fired, when, under which reasoning code. Read models expose cursorable trip lists and per-trip timelines for anything downstream that cares about liability graphs or fleet replay.

Hardening

The service layer is pinned on a modern Python runtime, ASGI-native workers, and co-located static delivery so the capture UI and the policy engine share origin—no cross-site identity theater, no split certificate stories for the live feed.

Disclaimer

Research and demonstration artifact—not certified for ADAS, FMVSS, or clinical vigilance. No claim of crash avoidance or medical diagnosis. Gaze and fatigue estimates are heuristics; mount, optics, and population shift eat your lunch.

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