RGB HD Cameras, Fisheye Cameras (Omnidirectional Model), 250° Radar (mmWave Model), LiDAR with Rolling Shutter Support, LWIR Thermal Cameras, and Ultrasonic Sensors
Model Interfaces : Simulink, ROS. Tool Interfaces : SUMO. Test Automation Support : Jenkins and Bamboo. Standards Compliance : OpenDRIVE and OpenSCENARIO.
Ready-to-use pass/fail criteria for AV and ADAS validation and certification, powerful custom rule creation, trend mining for large-scale simulations, and virtual AV certification aligned with regulatory requirements.
Pre-built scenario libraries for common autonomous driving functions, diverse terrain types and vehicle dynamics, and a comprehensive asset catalog including vehicles, pedestrians, cyclists, vegetation, buildings, and more.
More than 1,000 unique parameters configurable for specific vehicle models and dynamic behavior characteristics. Import customized 3D assets for dynamic objects and autonomous vehicles. Supports autonomous vehicle heights ranging from 1.5 to 4 meters, including custom tire diameters and track widths.
Photorealistic synthetic data generated using Deep Neural Networks (DNNs), pixel-level accurate annotations for every frame with real-world precision, and AI-driven interactive behavior profiles tailored to specific geographic environments.
Cognata’s autonomous driving and ADAS simulation platform supports the entire development lifecycle, including training, testing, validation, and certification, significantly reducing the time required to test and validate autonomous vehicle (AV) systems before commercial deployment.
Cognata’s off-road AV simulation is designed to test, train, and validate perception and control challenges in terrains without clearly defined road structures. Leveraging synthetic data within a digital twin environment, the platform delivers a new level of realism for defense-specific applications, including the training and validation of next-generation combat vehicles, manned combat systems, and robotic ground vehicles.
Cognata’s off-road AV simulation is designed to test, train, and validate perception and control challenges in unstructured environments. Using synthetic data within a digital twin environment, the platform provides a new level of realism for training and validating agricultural robots and automated farming equipment.
Cognata’s off-road AV simulation is designed to test, train, and validate perception and control challenges in unstructured terrains. Utilizing synthetic data within a digital twin environment, the platform provides a new level of realism for training and validating autonomous mining equipment, haul trucks, and other heavy industrial machinery.
Autonomous Mobile Robot (AMR) Simulation – Cognata provides warehouse simulation solutions for the rapid and cost-effective deployment of Autonomous Mobile Robots (AMRs) and Automated Guided Vehicles (AGVs), featuring customized dynamic assets for route obstacles and operational challenges.
Cognata’s 4D urban planning model integrates AI-powered mobility and traffic layers to support Vision Zero initiatives by analyzing road safety and traffic flow. The simulation platform creates digital twins that enable cities to test and evaluate traffic scenarios, improve urban planning, and optimize public safety.
