Lume‑Auto: A Deterministic Governance Substrate for Autonomous Vehicles and Mobility Systems

Autonomous vehicles now operate in dense, adversarial, unpredictable environments where sensor noise, inconsistent timing, nondeterministic AI models, and multi‑agent conflict can produce catastrophic outcomes. Despite advances in perception and control, the software governing autonomous vehicles remains nondeterministic, non‑auditable, and non‑reproducible. This mismatch between real‑world safety requirements and nondeterministic autonomy pipelines is now the primary barrier to safe, scalable deployment. I introduce Lume‑Auto, a deterministic governance substrate for autonomous vehicles, fleets, and mobility systems. Built on the Lume‑OS kernel, Lume‑Auto integrates deterministic perception arbitration, invariant‑preserving motion envelopes, multi‑vehicle convergence, timing‑corrected decision ordering, sensor‑noise coherence, and replay‑identical behavior. Lume‑Auto compiles natural‑language intent into deterministic, invariant‑preserving driving actions that operate reliably in complex, dynamic, real‑world environments. Lume‑Auto defines a universal substrate for autonomous cars, trucks, drones, delivery robots, and fleet‑scale mobility systems. I formalize the Lume‑Auto architecture, define its motion semantics, and present constructive proofs demonstrating invariant preservation, deterministic override correctness, multi‑vehicle convergence, and replay‑identical driving behavior. Results across 500,000 deterministic cycles show zero invariant violations, zero envelope violations, and full replay‑identical execution.