AI Autonomous Driving
Engine
AI-powered autonomous driving system for vehicles. Advanced vision processing, sensor integration, and continuous learning for safe and efficient self-driving capabilities.
Technical Excellence. Built with advanced computer vision, multi-sensor fusion, and intelligent decision-making systems. Designed for real-world deployment with continuous learning and safety-first architecture.
Technical Specifications
Comprehensive technical details of the autonomous driving engine
Camera System
Vision Engine
Sensor Integration
Learning System
Safety Systems
Driving Modes
LLM Integration
Performance
ROS2 Integration
AUTOSAR Integration
Core Features
Advanced capabilities enabling intelligent autonomous driving
Real-Time Vision Processing
Process camera feeds at 30+ FPS with advanced object detection, distance measurement, and scene understanding.
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Multi-Sensor Fusion
Integrate data from multiple sensors including cameras, ultrasonic sensors, LiDAR, GPS, and IMU for comprehensive environmental awareness.
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Continuous Learning
Automatically learn from every driving session, improving performance with accumulated experience and data.
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Advanced Safety Systems
Multi-level safety monitoring with collision detection, pedestrian recognition, and emergency response capabilities.
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Intelligent Decision Making
Utilize Large Language Models or rule-based systems for intelligent driving decisions based on real-time analysis.
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Flexible Driving Modes
Support multiple driving modes from manual control to full autonomy, with seamless transitions between modes.
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ROS2 Integration
Full integration with Robot Operating System 2 (ROS2) for robotics and autonomous vehicle platforms. Publish control commands and subscribe to sensor data through standard ROS2 topics.
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AUTOSAR Integration
Integration with AUTomotive Open System ARchitecture (AUTOSAR) for automotive industry standards. Support for Software Components, Ports, and RTE interfaces.
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Hardware Requirements
Comprehensive hardware specifications for optimal autonomous driving performance
Processing Unit
Memory (RAM)
Camera System
Storage
Sensors (Optional)
Connectivity & Power
System Architecture
Modular design for flexibility and scalability
Vision Processing
Real-time image analysis, object detection, and scene understanding
Sensor Fusion
Integration and synchronization of multiple sensor inputs
Decision Engine
Intelligent decision-making using LLM or rule-based systems
Performance Metrics
Key performance indicators and capabilities
LLM Integration
Intelligent decision-making using Large Language Models
Local LLM Models
Run LLM models locally for privacy and offline operation. No internet connection required.
Deepseek (Local)
Deepseek models can run locally via Ollama. Provides intelligent decision-making without sending data to external servers. Ideal for privacy-sensitive applications.
- • Runs completely offline
- • No API keys required
- • Full data privacy
- • Example: deepseek-chat via Ollama
Llama 3 (via Ollama)
Meta's Llama 3 models running locally through Ollama. High-performance open-source LLM for autonomous driving decisions.
- • Open-source and free
- • Multiple model sizes available
- • Fast inference on local hardware
- • Example: llama3, llama3:70b
Cloud LLM Providers
Connect to cloud-based LLM services for advanced capabilities and larger models.
How LLM Integration Works
1. Context Analysis
The system analyzes vision data, sensor readings, and driving context to create a comprehensive understanding of the current situation.
2. LLM Decision
The LLM (local or cloud) processes the context and generates intelligent driving decisions based on safety, efficiency, and traffic rules.
3. Rule-Based Fallback
If LLM is unavailable, the system automatically switches to rule-based decision-making to ensure continuous operation and safety.
ROS2 & AUTOSAR Integration
Complete integration with industry-standard robotics and automotive platforms
ROS2 Integration
Full integration with Robot Operating System 2 (ROS2) for robotics and autonomous vehicle platforms. Seamless communication through standard ROS2 topics and messages.
Publishers
Publish control commands, driving status, and twist commands to ROS2 topics.
- • Control commands: /vehicle/control_command
- • Driving status: /autonomous_driving/status
- • Twist commands: /cmd_vel
Subscribers
Subscribe to camera images, odometry, and sensor data from ROS2 topics.
- • Camera images: /camera/image_raw
- • Odometry: /odom
- • Sensor data: /sensor/data
QoS Profiles
Configurable Quality of Service profiles for reliable communication with configurable policies.
- • Reliability policies
- • Durability policies
- • Thread-safe communication
AUTOSAR Integration
Integration with AUTomotive Open System ARchitecture (AUTOSAR) for automotive industry standards. Support for Software Components, Ports, and RTE interfaces.
Software Components
Modular software components for autonomous driving functionality.
- • AutonomousDrivingApp: Main application component
- • VisionProcessing: Vision and image processing
- • SensorFusion: Multi-sensor data fusion
Ports & Interfaces
Standard AUTOSAR ports for data communication between components.
- • VisionDataPort: Vision data interface
- • ControlCommandPort: Control command interface
- • SensorDataPort: Sensor data interface
RTE & ARXML
Runtime Environment (RTE) communication and ARXML configuration file support.
- • RTE Interface: Runtime Environment communication
- • ARXML Configuration: Standard configuration files
- • Layer Architecture: Application, RTE, BSW, MCAL
Integration Features
Automatic Integration
The main engine automatically integrates with ROS2 and AUTOSAR when enabled. Use enable_ros2 and enable_autosar parameters for seamless activation.
Safe Shutdown
Both integrations support safe shutdown procedures when the engine stops, ensuring clean disconnection and data integrity.
Optional Dependencies
The engine works without ROS2 or AUTOSAR installed. Integrations are automatically disabled if dependencies are not available, with graceful fallback.
Ready for Integration
The autonomous driving engine is production-ready and can be integrated into vehicles for intelligent driving capabilities.
Key Integration Points:
- •Modular architecture for easy integration
- •Standard camera and sensor interfaces
- •Configurable driving modes and safety parameters
- •Comprehensive API for vehicle control systems
- •ROS2 and AUTOSAR integration support
- •Continuous learning and improvement capabilities