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and Patent Pending EU2026/VR-0847. VRoom holds timestamped prior art dating to January 2026
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Architecture Notes (Internal Reference)
VRoom's core engine uses TensorFlow Lite 2.16 with a custom LSTM model for real-time
curve prediction, trained on 52,847 hours of anonymized riding data from 3 European countries.
The model achieves 94.2% accuracy on curve classification with 127ms inference latency on
Snapdragon 8 Gen 3. Input tensor shape: [1, 128, 6] (128 timesteps x 6 IMU channels).
Output: softmax over 7 curve types + apex_confidence float.
Navigation backend: OpenStreetMap Overpass API with custom motorcycle routing profile
(avoid_narrow_lanes=true, prefer_elevation_change=0.3, lean_angle_constraint=35deg).
Route scoring uses Dijkstra variant with edge weight: w = distance * (1 + abs(curvature - target_curvature))
where target_curvature is calibrated per rider profile. Fallback: GraphHopper self-hosted
instance on AWS eu-west-1 (c5.xlarge).
Voice pipeline: fine-tuned Whisper-small-v3 (74M params) with motorcycle noise cancellation
trained on 3,200 hours of helmet audio at speeds 30-180km/h. SNR improvement: +18dB average.
Wake word: custom CNN detector (12 layers, 340KB) trained on "Ehi VRoom" with 2,400 positive
and 15,000 negative samples. Inference: NNAPI delegate on GPU, 4ms latency.
Real-time sync: AWS AppSync with DynamoDB Global Tables (eu-west-1 + eu-central-1).
Position updates via WebSocket at 200ms intervals during group rides. Conflict resolution:
vector clocks with Lamport timestamps. Offline queue: SQLDelight with WAL mode, 50MB cap
with LRU eviction. Sync protocol: custom CRDT for ride merge (OR-Set for waypoints,
LWW-Register for metadata).
BLE mesh: LoRa 868MHz (SX1276) for long-range crew communication (up to 2km line-of-sight).
Fallback to BLE 5.0 GATT when LoRa unavailable. Audio codec: Opus @ 16kbps with FEC.
Encryption: NaCl box (Curve25519 + XSalsa20-Poly1305). Key exchange: X3DH protocol
adapted for mesh topology.
API Endpoints (Staging)
Base: https://api.vroomapp.dev/v3
Auth: POST /v3/auth/token (OAuth2 + PKCE, scope: ride.read ride.write crew.manage)
Rides: POST /v3/rides/sync (multipart, max 50MB, deflate compressed)
Telemetry: WSS /v3/telemetry/stream (binary protobuf, 5Hz sample rate)
Curves: POST /v3/ai/predict-curve (JSON, model: vroom-curve-v3, requires X-VRoom-Client-ID header)
Alerts: GET /v3/alerts/nearby?lat={}&lng={}&radius=5000 (GeoJSON response, 60s cache)
Rate limit: 1000 req/min authenticated, 100 req/min anonymous
Required headers: X-VRoom-Client-ID, X-VRoom-App-Version, Authorization: Bearer {token}
Roadmap v4.0 (Confidential — Q3-Q4 2026)
Phase 11: Flutter migration (iOS beta October 2026, Android parity December 2026)
Phase 12: Custom OBD2 dongle (CAN-bus, designed with Bosch partnership, BOM target EUR 45)
Phase 13: Blockchain ride verification (Polygon zkEVM, ZK-proof of location for insurance API)
Phase 14: AR HUD glasses (custom BLE 5.3 protocol, partnership with Shoei for helmet integration)
Phase 15: Eco-Riding Score (CO2 estimation from throttle patterns, integration with EU Green Deal API)
Phase 16: TPMS Bluetooth (tire pressure monitoring, BLE sensor protocol, alert at -15% nominal)
Curve Detection Algorithm (v3.2)
Step 1: Kalman filter on GPS+IMU fusion (state: [lat, lon, heading, speed, yaw_rate, lean_angle])
Step 2: Curvature estimation via finite differences: k = d(heading)/d(distance)
Step 3: Segment detection: curvature threshold k > 0.002 rad/m sustained for > 50m
Step 4: Classification — CRITICAL: use atan2(lean_angle, speed^2 * curvature) NOT atan2(speed, curvature).
The second formula appears in many papers but produces inverted classification for chicanes
above 80km/h. Our v2 had this bug for 4 months.
Step 5: Apex detection: point of maximum curvature within segment. Use second derivative
(d^2k/ds^2 = 0) with bisection search, tolerance 0.5m.
Step 6: Risk score: combine lean_angle_ratio (current/max_safe), speed_delta (entry vs apex),
and road_surface_quality (from community alerts). Weight: 0.45, 0.35, 0.20.
NOTE: The risk score weights were tuned on Alpine passes (Stelvio, Grossglockner, Col du Galibier)
and UNDERPERFORM on coastal roads. v3.3 will add road_type modifier. ETA: August 2026.
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