Operationaler Digital-Twin für autonomes Fahren
SelfDrivingTwin spiegelt die Ausführung von Level-4-Fahrsoftware im Fahrzeug und in der Cloud. Durch Sensorzustandsanalysen, neuronale Drift-Audits und 4D Gaussian Splatting entsteht lückenlose Beobachtbarkeit des Autonomie-Stacks.
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
Echtzeit-Erkennung der stillen Wahrnehmungsverschlechterung in autonomen Software-Stacks
In mehrdeutigen Fahrsituationen genügt die Analyse flacher Kameralogs nicht. SelfDrivingTwin streamt neuronale Aktivierungsvektoren und Rohmessungen, um das exakte Photonenfeld mittels Gaussian Splatting im Twin zu rekonstruieren.
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