Technology Overview
VitalDrive’s Health Intelligence Engine (VD-HIE) is architected for safety-critical, certifiable deployment in vehicles.
Architecture Overview
VitalDrive® integrates in-vehicle physiological sensors with an edge-to-cloud intelligence pipeline designed to operate reliably in noisy, real-world driving environments. The system combines deterministic safety logic with probabilistic machine-learning inference to enable early risk detection and controlled intervention.
Current Implementation
Status
Current Implementation Status (Dec 2025)
✔ Rule-based risk engine operational
✔ Spec-clean Risk API
✔ End-to-end JSON demonstration pipeline
⚠ Machine-learning models: architecture defined, training in Phase-2
⚠ Real-world physiological datasets: academic collaboration phase
Why VITALDRIVE® Uses a Hybrid Intelligence
Architecture
> Safety Boundaries Must Be Deterministic
Pure machine-learning systems excel at pattern recognition but struggle with uncertainty, out-of-distribution events, and explainability. In safety-critical contexts such as medical or automotive systems, certain boundaries must never be violated. Rule-based logic defines these non-negotiable safety constraints.
> Machine Learning Provides Evidence, Not Judgment
Machine-learning models within VD-HIE are used to extract probabilistic evidence from complex physiological and contextual signals. These models identify subtle precursors and correlations but do not make final safety decisions independently.
> Certifiable Decision-Making Requires Predictable Behaviour
In mature safety domains such as aviation, medical devices, and automotive functional safety, perception may be learned, but decision-making must remain bounded and explainable. VD-HIE separates signal interpretation from judgment, ensuring predictable failure modes, deterministic escalation paths, and post-event traceability.
