---
license: mit
tags:
- onnx
- time-series
- imu
- navigation
- dead-reckoning
---

# COAST-VNet-1 (Tiny)

Edge-native, frequency-decoupled AI model for two-wheeler dead reckoning in GNSS-denied environments (urban canyons, tunnels, underpasses).

## Model Details
- **Architecture:** Frequency-Decoupled CNN-GRU
- **Parameters:** 96,086
- **Size:** 392 KB
- **Format:** ONNX (Optimized for Mobile/Edge CPU)
- **Input:** (20, 6) tensor — 2.0s window @ 10 Hz (ax, ay, az, gx, gy, gz)
- **Output:** (6) — Forward speed, Yaw rate, Roll/Pitch residuals, and Log-Variance (Uncertainty).

## Training Data
Trained on the IO-VNBD Dataset (23 drives). Ground truth labels were sourced exclusively from the vehicle CAN-bus, completely bypassing noisy phone GPS signals to ensure the model learns true kinematics.

## Use Case
Designed to maintain navigation continuity for ride-hailing and emergency response fleets. The model mathematically separates low-frequency vehicle motion (lean/acceleration) from high-frequency road and engine vibration.

## Performance
- **Inference Speed:** 8.3 microseconds per step (19,682 Hz on standard CPU).
- **Accuracy Gain:** 2.02x lower median position error compared to naive dead reckoning over 43 real-world GPS outages.