MORAI provides a digital twin simulation platform that utilizes high-definition mapping and a physics engine to create realistic test environments for autonomous vehicles, urban air mobility, and maritime systems. This technology enables developers to efficiently validate and optimize their autonomous systems through scalable, cost-effective simulations based on real-world data.
Funding
$22.6M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Developing and validating autonomous systems like vehicles, urban air mobility solutions, and maritime systems requires extensive testing, which can be costly and time-consuming in real-world environments. Physical testing also poses safety risks and limitations in replicating diverse and complex scenarios.
Solution
MORAI offers a digital twin simulation platform that enables developers to create realistic and scalable virtual environments for testing and validating autonomous systems. By leveraging high-definition mapping data and a physics engine, the platform accurately replicates real-world conditions, sensor behavior, and vehicle dynamics. This allows for comprehensive testing of autonomous systems across various scenarios, including edge cases and complex traffic situations, in a safe, cost-effective, and repeatable manner. The platform supports cloud-based simulation, enabling concurrent testing and rapid iteration, and is ISO 26262 certified for automotive safety integrity.
Target Audience
The primary target audience includes developers and manufacturers of autonomous vehicles, urban air mobility solutions, maritime systems, and autonomous mobile robots, as well as researchers and organizations involved in traffic management and infrastructure development.
Features
- High-definition mapping and physics engine for realistic environment simulation
- Support for various sensor models, including camera, LiDAR, GPS, radar, and IMU
- Scenario creation from real-world data and edge-case scenario generation
- Cloud-based simulation for scalable and concurrent testing
- Vehicle-in-the-loop (VIL) testing capabilities for mixed reality simulations
- Automatic annotation functionality for dataset generation (KITTI, Cityscape)
- Real-time traffic management with C-ITS data integration
- Customizable ego robot modeling for autonomous mobile robot development
- Support for Air Traffic Management (ATM) and Unmanned Traffic Management (UTM) scenarios