Autonomous Driving Operational Digital Twin
SelfDrivingTwin mirrors the runtime execution of Level 4 autonomous software on the edge and in the cloud. By continuously recording sensor degradation, auditing neural layer activations for out-of-distribution drift, and re-rendering real-world driving scenes in 4D Gaussian splats, it delivers complete runtime observability.
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
Real-Time Detection of Silent Perception Degradation in Autonomous Stacks
When an autonomous vehicle encounters an ambiguous traffic situation, post-mortem analysis of flat camera logs is inadequate. SelfDrivingTwin continuously streams neural activation vectors alongside raw range measurements. Back in the digital twin, neural radiance fields and Gaussian splatting reconstruct the exact photon field.
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
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