morai.ai: Advanced Digital Twin Simulation for Autonomous Mobility

morai.ai is a cutting-edge digital twin simulation platform developed by MORAI Inc., designed to accelerate the development and validation of autonomous systems across multiple domains including autonomous vehicles (Drive), urban air mobility (Air), robotics, maritime autonomous surface ships (MASS), and smart traffic management. With high-definition (HD) maps, a robust physics engine, and fully customizable sensor models (cameras, LiDAR, GPS, radar, IMUs), the platform delivers lifelike, scalable, and safe virtual environments for testing complex and edge-case scenarios. It supports cloud-based simulation with concurrent runs, enabling thousands of test iterations without hardware constraints, and integrates with industry standards like ISO 26262 (ASIL D certified), ISO 21448 (SOTIF), Euro NCAP, UNECE R157, OpenSCENARIO, OpenODD, and ASAM OpenX for seamless, compliant validation. The platform excels in scenario generation from real-world data, re-simulation of failed cases, and virtual homologation, significantly reducing testing mileage and development time by up to 50%. Additionally, morai.ai powers education through the MORAI Academy Suite, offering curriculum, textbooks, virtual labs, and competition support in collaboration with leading institutions. It also enables real-time traffic monitoring and optimization via integration with C-ITS and RSA data, supporting both urban and infrastructure applications.

Key Features:

• High-definition (HD) map-based 3D digital twin environments
• Realistic physics engine for accurate vehicle and robot dynamics
• Fully configurable and customizable sensor models (cameras, LiDAR, GPS, radar, IMUs)
• Cloud-based simulation with support for thousands of concurrent runs
• AI-assisted and real-world data-driven scenario generation
• Edge case and loop testing for robust algorithm validation
• Support for ISO 26262 (ASIL D certified) and ISO 21448 (SOTIF) compliance
• Integration with Euro NCAP, UNECE R157, NHTSA, RSS, and KNCAP safety standards
• Compatibility with OpenSCENARIO, OpenODD, and ASAM OpenX standards
• Mixed Reality Vehicle-in-the-Loop (VIL) testing
• Dataset generation with automatic annotation, weather, and lighting control
• Support for KITTI and Cityscapes formats for deep learning training
• Real-time traffic flow simulation compatible with VISSIM and SUMO
• Integration with C-ITS and Road Safety Audit (RSA) data for urban monitoring
• Layer on/off functionality for dynamic environment visualization
• Customizable ego robot modeling (wheeled robots, manipulators) for robotics applications
• Simulation of eVTOL flight operations, vertical takeoff/landing, and collision avoidance
• Air Traffic Management (ATM) and Unmanned Traffic Management (UTM) scenario simulation
• Digital twin solutions for aerospace maintenance (Smart MRO) and urban infrastructure
• Re-simulation of failed scenarios to refine and improve algorithms
• 3D-based monitoring of blind spots and shadow zones in urban environments
• MORAI Academy Suite for education, including curriculum, lecture materials, textbooks, and competition support
• Virtual testing environments for autonomous driving and robotics research
• Seamless co-simulation with scale model vehicles for real-world validation
• Unified digital twin system integrating disparate data sources to eliminate data silos
• Six-layer scenario taxonomy based on PEGASUS for structured scenario development
• Coverage metrics and reusable scenario databases for comprehensive testing

Pricing: morai.ai offers a freemium model with access to foundational tools and educational resources, while enterprise and specialized simulation solutions are available through subscription tiers tailored to industry needs.

Conclusion: With its powerful combination of digital twin fidelity, regulatory compliance, and multi-domain scalability, morai.ai stands as a transformative simulation platform for the future of autonomous mobility, empowering developers, researchers, and educators to innovate safely, efficiently, and at scale.