Gemelo Digital Operacional de Conducción Autónoma
SelfDrivingTwin refleja la ejecución en tiempo real del software Nivel 4 en el vehículo y la nube. Monitorea la degradación de sensores, audita activaciones neuronales y reconstruye escenas viales en 4D para una observabilidad integral.
Self-Driving Twin Core Zonal Architecture
Central Controller: Self-Driving Twin Core — 4D World Reconstruction Engine
Zonal Control Units & TSN Ring
- Raw Sensor Stream Compressor (H.265/DRA)
- Neural Activation Shadow Observer
- Continuous Sensor SoH Tracker
- 4D Spatio-Temporal Occlusion Checker
- Instant Shadow-Mode Disengagement Logger
Detección en Tiempo Real de la Degradación Silenciosa de la Percepción en Pilas Autónomas
Cuando un vehículo autónomo afronta situaciones ambiguas, los vídeos planos son insuficientes. SelfDrivingTwin transmite vectores de activación neural y mediciones brutas para reconstruir en la nube el campo de luz exacto del suceso.
Core Engineering Areas
- Real-Time Sensor Health Tracking — Computing point cloud density drop-offs and camera optical flow degradation in real time to quantify physical sensor health degradation.
- Neural Activation Shadow Monitoring — Projecting high-dimensional latent vectors from onboard vision transformers to identify out-of-distribution driving environments.
- 4D Spatio-Temporal Reconstruction — Reconstructing exact multi-agent driving interactions into 3D Gaussian Splatting and NeRF representations for post-hoc safety certification.
Technical Articles
Related Cluster Architecture Specifications
- AutomotiveSDV — Full SDV Stack & Reference Architecture
- A2HCard — Agent-to-Human (A2H) Interface Cards & In-Cabin Autonomous Interaction Manifests
- A2HumanCard — Standardized In-Vehicle AI Agent Capability Discovery, Trust & Authorization Cards
- AIDrivenSynBio — AI-Driven Synthetic Biology for Automotive In-Cabin Air Purification, Bio-Engineered Pathogen Scavengers & Molecular Biosensors