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.