software-architecture.md

Automotive LLM System - Software Architecture

Operating System

Raspberry Pi OS (64-bit)

• Debian-based Linux optimized for Pi hardware
• Custom automotive boot configuration
• Read-only root filesystem for reliability
• Systemd services for automotive processes

Local LLM Selection

Primary Model Options:

1. Llama 3.1 8B Instruct (Recommended)

   • 8 billion parameters
   • Excellent instruction following
   • Automotive domain fine-tuning potential
   • ~5GB model size

2. Phi-3 Medium (14B)

   • Microsoft's efficient model
   • Good reasoning capabilities
   • Optimized for edge deployment
   • ~8GB model size

3. Mistral 7B Instruct

   • Fast inference speed
   • Good automotive knowledge
   • Commercial use friendly
   • ~4GB model size

Inference Engine

Ollama

• Local LLM serving platform
• Optimized for edge devices
• Model quantization support (Q4_K_M recommended)
• REST API for integration
• Automatic model management

Hailo Optimization

• Hailo Runtime integration
• Neural network acceleration
• Optimized model compilation
• Real-time inference performance

Core Software Stack

Voice Processing Pipeline

Speech-to-Text (STT)

• OpenAI Whisper (base model)
• Hailo-optimized for real-time processing
• Offline operation capability
• Automotive noise filtering

Text-to-Speech (TTS)

• Piper TTS (lightweight)
• Natural voice synthesis
• Low latency for real-time response
• Multiple voice options

Wake Word Detection

• Picovoice Porcupine
• Custom wake words: "Hey Car", "Vehicle Assistant"
• Always-on listening with low power
• Hardware acceleration support

Vehicle Integration Layer

OBD-II Interface

• Python-OBD library
• Real-time diagnostic data
• Fault code interpretation
• Performance monitoring

CAN Bus Communication

• python-can library
• Vehicle-specific protocol adapters
• Message filtering and parsing
• Safety-critical communication

Control Systems

HVAC Management

• Temperature control logic
• Fan speed adjustment
• Mode selection (heat/cool/auto)
• Zone-based control for multi-zone systems

Engine Management

• Performance parameter monitoring
• Tuning parameter adjustment
• Safety limit enforcement
• Real-time diagnostics

Lighting Control

• Interior lighting automation
• Exterior lighting management
• Ambient lighting customization
• Emergency lighting protocols

Application Architecture

Core Services

┌─────────────────────────────────────────┐
│             Main Controller             │
├─────────────────────────────────────────┤
│  Voice Manager  │  Vehicle Interface    │
│  LLM Controller │  Safety Monitor       │
│  Device Manager │  System Health        │
└─────────────────────────────────────────┘

Main Controller

• Central orchestration service
• Inter-service communication
• State management
• Error handling and recovery

Voice Manager

• Audio capture and processing
• Wake word detection
• STT/TTS pipeline management
• Conversation context handling

LLM Controller

• Model loading and management
• Prompt engineering for automotive context
• Response generation and filtering
• Context-aware conversations

Vehicle Interface

• OBD-II/CAN bus communication
• Vehicle state monitoring
• Command execution
• Data logging

Safety Monitor

• Critical system oversight
• Emergency protocols
• Fail-safe mechanisms
• Driver attention monitoring

Data Flow

1. Voice Input → Wake Word Detection → STT
2. Text Processing → Intent Recognition → LLM Processing
3. Command Generation → Safety Validation → Vehicle Control
4. Response Generation → TTS → Audio Output
5. Continuous Monitoring → Vehicle State → Context Updates

Development Framework

Primary Language: Python 3.11+ Key Libraries:

• asyncio - Asynchronous processing
• fastapi - REST API framework
• pydantic - Data validation
• sqlalchemy - Database ORM
• redis - Caching and message queue

Container Architecture:

• Docker containers for service isolation
• Docker Compose for multi-service orchestration
• Volume mounting for persistent data
• Health checks and auto-restart

Database Architecture

SQLite (Primary)

• Vehicle configuration
• User preferences
• Command history
• System logs

Redis (Cache/Queue)

• Real-time vehicle state
• Message queuing
• Session management
• Performance metrics

Security Architecture

Authentication

• Voice biometrics (optional)
• PIN-based access for sensitive functions
• Multi-factor authentication for setup

Authorization

• Role-based access control
• Command-level permissions
• Safety-critical function restrictions
• Emergency override protocols

Data Protection

• Local data encryption at rest
• Secure communication protocols
• No cloud data transmission
• Privacy-focused design

Configuration Management

Vehicle Profiles

• Make/model-specific configurations
• Custom command mappings
• Performance parameter limits
• Safety protocol definitions

User Preferences

• Voice recognition training
• Personalized responses
• Comfort settings
• Usage patterns

Monitoring and Logging

System Health

• Resource utilization monitoring
• Temperature and performance metrics
• Error rate tracking
• Predictive maintenance alerts

Audit Logging

• All voice commands logged
• Vehicle control actions recorded
• System access events
• Safety incident documentation

Update and Maintenance

Over-the-Air Updates

• Secure update delivery
• Rollback capability
• Staged deployment
• Minimal downtime updates

Model Updates

• LLM model versioning
• A/B testing framework
• Performance regression detection
• Automated rollback on failure

Performance Optimization

Model Quantization

• INT4/INT8 quantization for speed
• Dynamic quantization during inference
• Memory usage optimization
• Batch processing for efficiency

Caching Strategy

• Frequent command caching
• Vehicle state caching
• Response template caching
• Predictive loading

Development Tools

• Testing: pytest, automotive simulation
• Monitoring: Prometheus, Grafana
• Debugging: Remote debugging capability
• Documentation: Automated API documentation