01 / IMPLEMENTATION & CONTRIBUTION
What the work involves
Deployed and validated real-time inference for eight safety-relevant detector classes. Designed the hierarchical alert state machine and vehicle-context gating, persisted per-camera calibration profiles, and built the diagnostic lab CLI.
Technical depth
Eye closure, yawning, distraction, phone use, smoking, face loss, lens occlusion, and eye anomaly detection; hierarchical face-loss and lens-occlusion fallbacks; speed, ignition, and gear gating; per-camera calibration; shadow-mode hard-case capture, human review, and replay-based metrics.
The project family
dms02 / RESULTS
What came out of it
Validated on real cabin video and shipped a 616-test pytest suite with replay-driven regressions, inference-cadence checks, and per-detector coverage metrics in lockstep with model development.
03 / SUPPORTING EVIDENCE
Follow the source
Implementation notes, project records, and supporting artifacts.
Source context & project scope
Employment, on-device deployment, and cabin-video validation reflect Donald’s latest internship record. The 616-test suite is a project result, not a new website-run test.
The supplied record does not claim safety certification, verified CAN integration, or quantified fleet-wide sensitivity/specificity.
Deployed and validated real-time on-device driver-state monitoring, connecting safety-relevant alerts, low-power edge inference, and replay-driven validation. Deployed and validated eight safety-relevant detector classes: eye closure, yawning, distraction, phone use, smoking, face loss, lens occlusion, and eye anomaly. Shipped a 616-test pytest suite with replay-driven regressions, inference-cadence checks, and per-detector coverage metrics alongside model development. Designed the alert state machine with hierarchical face-loss and lens-occlusion fallbacks. Speed, ignition, and gear gating suppress false alarms in non-driving states; persisted per-camera calibration profiles support fleet-grade traceability. Fit the model pipeline to the low-power CV181x edge SoC by tightening inference cadence and per-stage compute budgets. Validated end-to-end on real cabin video and built the lab CLI for shadow-mode hard-case capture, human review, and replay-based metric reporting.
CANDIDATE · 2025 – PresentArchitecture, eight detection states, calibration workflow, shared lab and historical test count.
SOURCE · 2026-09-17FrameContext and VehicleContext explicitly carry source timestamps and motion-alert gating.
SOURCE · 2026-09-17Secondary hand-inference scheduling and shared detector orchestration.
SOURCE · 2026-09-17