Genesis Automotive SDK

Production-grade automotive telemetry SDK with advanced sensor fusion, OBD-II diagnostics, and real-time performance analysis

Features • Architecture • Installation • Usage • Documentation • Contributing

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🚗 Overview

Genesis Automotive SDK is a comprehensive, production-ready telemetry platform designed for high-performance automotive applications. Built on a cross-platform C++ core with native iOS and Android SDKs, it provides:

• Advanced Sensor Fusion: 12-state Extended Kalman Filter (EKF) fusing GNSS, IMU, Computer Vision, and OBD-II data
• Multi-Tier Fallback System: Graceful degradation from full-fidelity (±0.01s) to dead reckoning modes
• On-Device AI: CoreML/TensorFlow Lite models for gear shift detection and anomaly recognition
• OBD-II Diagnostics: Universal protocol support with 10,000+ DTC definitions
• Real-Time Performance: Sub-10ms latency for competitive motorsports applications

✨ Features

Sensor Fusion Engine

• ✅ Extended Kalman Filter (EKF): 12-dimensional state vector with IMU bias estimation
• ✅ GNSS Integration: WGS84 coordinate transforms with adaptive noise tuning
• ✅ Computer Vision: Lucas-Kanade optical flow with RANSAC outlier rejection
• ✅ IMU Processing: High-frequency (100+ Hz) accelerometer and gyroscope integration
• ✅ Barometric Altitude: Pressure-based altitude compensation

Tiered Fallback System

Tier	Mode	Accuracy	Sensors Active
Tier 1	Full Fidelity	±0.01s	GNSS + IMU + Vision + OBD
Tier 2	Vision Degraded	±0.05s	GNSS + IMU + OBD
Tier 3	Dead Reckoning	±0.2s	IMU + OBD only
Tier 4	Emergency	Last known	Position hold + warning

OBD-II Diagnostics

• ✅ ELM327 Bluetooth/WiFi support
• ✅ CAN bus decoding (11-bit & 29-bit identifiers)
• ✅ 10,000+ manufacturer-specific DTCs
• ✅ Real-time PID monitoring (RPM, MAF, throttle position, etc.)
• ✅ Freeze frame data capture

On-Device Machine Learning

• ✅ Gear shift detection (CoreML for iOS, TensorFlow Lite for Android)
• ✅ Launch detection and traction loss identification
• ✅ Anomaly detection for predictive maintenance
• ✅ Sub-50ms inference latency

🏗️ Architecture

GenesisAutomotiveSDK/
├── Core/                    # Cross-platform C++ engine
│   ├── Fusion/
│   │   ├── GenesisEKF.hpp/.cpp          # Extended Kalman Filter
│   │   ├── SensorFusion.hpp/.cpp        # Multi-sensor integration
│   │   └── StateEstimator.hpp/.cpp      # State management
│   ├── Vision/
│   │   ├── OpticalFlow.hpp/.cpp         # Lucas-Kanade + RANSAC
│   │   ├── FeatureTracker.hpp/.cpp      # Feature point tracking
│   │   └── QualityAssessor.hpp/.cpp     # Vision quality metrics
│   ├── Diagnostics/
│   │   ├── OBDProtocol.hpp/.cpp         # Universal OBD-II
│   │   ├── CANDecoder.hpp/.cpp          # CAN frame parser
│   │   ├── DTCDatabase.hpp/.cpp         # 10,000+ DTC definitions
│   │   └── ManufacturerPIDs.hpp/.cpp    # OEM-specific PIDs
│   └── AI/
│       ├── ShiftDetection.hpp/.cpp      # Gear shift ML models
│       └── AnomalyDetection.hpp/.cpp    # Predictive maintenance
│
├── Platform/
│   ├── iOS/
│   │   ├── GenesisSDK.swift             # Public Swift API
│   │   ├── SensorManager.swift          # CoreLocation/CoreMotion
│   │   ├── ELM327Manager.swift          # Bluetooth OBD-II
│   │   └── Bridge/
│   │       └── CoreBridge.mm            # Objective-C++ bridge
│   └── Android/
│       ├── GenesisSDK.kt                # Public Kotlin API
│       ├── SensorManager.kt             # FusedLocation/SensorManager
│       ├── ELM327Manager.kt             # Bluetooth OBD-II
│       └── jni/
│           └── CoreBridge.cpp           # JNI bridge
│
├── Cloud/                   # Optional cloud services
│   ├── FastAPI/
│   │   ├── api.py                       # Telemetry ingestion
│   │   ├── models.py                    # Pydantic schemas
│   │   └── ai_engine.py                 # LLM co-pilot
│   └── Firebase/
│       ├── functions/                   # Cloud Functions
│       ├── firestore.rules              # Security rules
│       └── storage.rules
│
└── Documentation/
    ├── API_Reference.md
    ├── Integration_Guide.md
    └── Performance_Tuning.md

📦 Installation

iOS (CocoaPods)

# Podfile
pod 'GenesisAutomotiveSDK', '~> 1.0'

iOS (Swift Package Manager)

dependencies: [
    .package(url: "https://github.com/TaylorsBar/genesis-automotive-sdk.git", from: "1.0.0")
]

Android (Gradle)

dependencies {
    implementation 'com.cartelworx:genesis-sdk:1.0.0'
}

Build from Source

# Clone repository
git clone https://github.com/TaylorsBar/genesis-automotive-sdk.git
cd genesis-automotive-sdk

# Build C++ core
mkdir build && cd build
cmake ..
make -j$(nproc)

# Run tests
ctest --output-on-failure

🚀 Usage

iOS (Swift)

import GenesisSDK

// Initialize SDK
let genesis = GenesisSensorManager()

// Start high-performance capture
genesis.startHighPerformanceCapture()

// Access fusion metrics
let metrics = genesis.coreBridge.getFusionMetrics()
print("Current Tier: \(metrics.current_tier)")
print("Position Uncertainty: \(metrics.position_uncertainty_m)m")

// Get state vector
let state = genesis.coreBridge.getStateVector()
let velocity = state.segment(3, 3) // [vx, vy, vz]
print("Velocity: \(velocity.norm()) m/s")

Android (Kotlin)

import com.cartelworx.genesis.GenesisSDK

// Initialize SDK
val genesis = GenesisSensorManager(applicationContext)

// Start capture
genesis.startHighPerformanceCapture()

// Subscribe to updates
genesis.fusionMetrics.observe(this) { metrics ->
    Log.d("Genesis", "Tier: ${metrics.currentTier}")
    Log.d("Genesis", "Position Uncertainty: ${metrics.positionUncertaintyM}m")
}

C++ (Direct)

#include "GenesisEKF.hpp"

using namespace CartelWorx;

// Initialize EKF
GenesisEKF ekf;

// Provide initial GNSS lock
GNSSMeasurement initial_gnss;
initial_gnss.latitude = -37.7749;
initial_gnss.longitude = 175.2834;
initial_gnss.altitude = 50.0;
ekf.initialize(initial_gnss);

// High-frequency prediction loop (100+ Hz)
while (running) {
    IMUMeasurement imu = readIMU();
    ekf.predict(imu, timestamp);
    
    // Update with GNSS when available (1-10 Hz)
    if (gnss_available) {
        ekf.updateGNSS(gnss_data);
    }
    
    // Update with vision (30+ Hz)
    if (vision_available) {
        ekf.updateVision(vision_data);
    }
    
    // Get fused state
    StateVector state = ekf.getState();
    FusionMetrics metrics = ekf.getMetrics();
}

📚 Documentation

• API Reference - Complete API documentation
• Integration Guide - Step-by-step integration instructions
• Performance Tuning - Optimization guidelines
• Examples - Sample projects for iOS, Android, and C++

🎯 Use Cases

• Motorsports Timing: Professional-grade lap timing and performance analysis
• Fleet Management: Real-time vehicle tracking and diagnostics
• Autonomous Vehicles: Sensor fusion and state estimation for L2+ ADAS
• Insurance Telematics: Usage-based insurance (UBI) data collection
• Performance Tuning: Dyno-less power measurement and diagnostics

🔬 Technical Specifications

Sensor Fusion Performance

• Latency: <10ms end-to-end (sensor to state output)
• Update Rate: 100+ Hz (IMU prediction), 10 Hz (GNSS update), 30 Hz (vision)
• Accuracy: ±0.01s (Tier 1), ±0.05s (Tier 2), ±0.2s (Tier 3)
• Position Drift: <1m per 30 seconds (dead reckoning mode)

System Requirements

iOS

• iOS 14.0+
• iPhone 8 or newer (A11 Bionic+ for on-device ML)
• GPS + GLONASS/Galileo receiver
• CoreMotion + CoreLocation access

Android

• Android 8.0 (API 26)+
• ARMv8-A or newer
• GPS + GLONASS/Galileo receiver
• Location + Sensor permissions

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

1. Install dependencies:

   • C++17 compiler (GCC 7+, Clang 5+, MSVC 2017+)
   • CMake 3.15+
   • Eigen 3.3+
   • OpenCV 4.0+ (for vision module)

2. Build and test:

   mkdir build && cd build
   cmake -DCMAKE_BUILD_TYPE=Debug ..
   make -j$(nproc)
   ctest --verbose
   

3. Format code:

   clang-format -i Core/**/*.{hpp,cpp}
   

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Dragy Performance: Inspiration for high-accuracy GPS-based timing
• RTKLIB: GNSS positioning algorithms
• Eigen: Linear algebra library
• OpenCV: Computer vision algorithms

📞 Support

• Documentation: https://genesis-sdk.cartelworx.com
• Issues: GitHub Issues
• Email: support@cartelworx.com
• Discord: CartelWorx Community

🗺️ Roadmap

• Q1 2025: Production 1.0 release
• Q2 2025: Web dashboard for telemetry visualization
• Q3 2025: Real-time AI co-pilot integration (Gemini Live API)
• Q4 2025: Autonomous vehicle sensor suite expansion

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Built with ❤️ by CartelWorx | KC Speed Lab

Powering the next generation of automotive intelligence