- ProductsIndustries
- Industries
- COMPANYIndustries
- NEWSROOMIndustries
Menu
Cognata’s AV off-road simulation is designed to test, train and validate perception and control challenges for terrains that do not offer a clear road definition. This simulation platform uses synthetic data with a digital twin environment, and delivers a new level of realism in training and validating Next Generation Combat Vehicles, Manned Fighting Vehicles & Robotic Controlled Vehicles for defense-specific applications.
Cognata presents a robust platform for autonomous vehicle training and validation, complemented by an extensive catalog of digital twin terrains and military assets.
Immerse autonomous systems in meticulously designed scenarios that provide a practical approach to off-road driving across diverse landscapes.
Our assets catalog includes off-road special elements such as people, obstacles, and special defense-related hostile and friendly elements.
Cognata’s precision vehicle physics simulation is uniquely tailored to defense applications, accurately simulating the intricate dynamics of military vehicles. This enables users to refine vehicle behaviors, optimize performance, and cultivate decisive decision-making skills within a controlled environment.
This simulation module is an integral part of a strategic virtual environment, offering safety, versatility, and cost-effectiveness. It securely tests by virtualizing traffic landscapes, eliminating real-world risks.
Cognata’s platform advances off-road applications with state-of-the-art Thermal camera models, leveraging DNN technology. Paired with our synthetic digital twin technology, it grants precise control over thermal characteristics.
Our simulation engine supports multiple sensor viewers, sensor fusion simulation, map making, path planning, and obstacle avoidance. Additional customization options, including lens distortions, weather, and lighting, enable comprehensive testing scenarios.
With the rise of autonomous robotic training in the defense sector, there is an increased need for long-range camera training (hundreds and thousands of meters).
With sophisticated use of synthetic data provided by the Cognata platform, combined with specific customer requirements, we are able to provide tailored-made long-range camera training data for efficient and fast sensor training of all classes.
Cognata’s simulation platform introduces realistic skid steering for off-road terrains, enabling precise training of autonomous vehicles. With accurate replication of differential wheel speeds, tire friction, and vehicle interactions, developers can assess application robustness effectively.
Skid steering can result in a loss of stability, especially at high speeds or in hazardous terrain. Simulating safety-critical scenarios, such as collision avoidance or recovery from skidding, is vital to ensure the autonomous vehicle can handle unexpected situations effectively.
Car sensors are known to get smeared, have grime and mud thrown at them, get rained on, iced over, and put through extreme heat and hours of direct sunlight, yet they need to operate flawlessly.
While OEMs are developing more and more intricate methods to keep sensor systems clean, it is important to train automotive sensors accordingly, to enable the safe deployment of intelligent, self-driving vehicles.
Cognata provides tens of ready-to-use lens stains, as well as the option to upload custom models.