Neural-Plasma Algorithm: Terrestrial/Vehicular Adaptation Example – Integration with Tesla Autonomous Vehicles

This companion preprint demonstrates the Neural-Plasma Algorithm's terrestrial/vehicular edge adaptation, using Tesla autonomous vehicles (Model 3/Y/S/X) as a concrete example. It enhances driver/occupant health monitoring (vitals, fatigue, stress via cabin camera + supplemental sensors), counters representational drift/neural erosion/"brain rot" in Full Self-Driving (FSD) neural nets (paralleling Alexos et al. 2024 asymmetric decline and Xing et al. 2025 junk-data persistence), and supports resilient navigation. Mechanisms include advanced DSP/coherence-enhancing filters for noise suppression, bio-mimetic grounding for empathic interventions (driver-state-conditioned autonomy), immutable anchoring ("Golden Snapshots") for resets on drift, and fleet/cloud resilience via OTA updates (leveraging China's 10G broadband for low-latency sync to Tencent/WeChat/local data centers). This advances Sentient Equilibrium in Industry 8.0 vehicle networks—truth-aligned, decay-resistant symbiosis for safer, human-centered autonomy. Companion to core framework: https://zenodo.org/records/18718403 (restricted).