Content Creation

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content creation

Scene editor and functionality

rFpro allows you to populate your virtual test scenarios with intelligent traffic and pedestrians acting just as they would in the real world. This is critical for the testing and development of ADAS, autonomous systems and RDE.

Actors in the scenario can be controlled by AI following the rules of the road or specific actions can be conducted, such as a potential collision at an intersection. These actions can be easily controlled using third-party providers, such as CarMaker Traffic.

It is also possible to have numerous vehicles and pedestrians controlled by humans, networking multiple simulators together in a single environment. Humans make mistakes and are less predictable than computers, this unpredictability makes humans perfect for testing autonomous systems.

Our weather and traffic interfaces are deterministic, so repeatable tests and experiments can be created, or repeated with controlled changes, to train deep learning systems and to provide a regression testing environment for AI driver models.

3DS Max integration

The weather can severely impact the performance of ADAS and autonomous systems so it is important to thoroughly test this in a safe, controlled environment. In rFpro there is no need to wait for the right weather to come or try to artificially create it, the weather and atmospheric conditions can be either live-tuned or pre-programmed. Clouds, precipitation levels, snow, fog, intensity of light can all be varied and controlled.

The position of the sun and the moon is accurate to the latitude and longitude of the real location that is being replicated, so shadows and reflections are cast in the same way given a certain time of day. This makes it easier to more accurately replicate scenarios in the real world to correlate your results. This is particularly important for the testing and training of ADAS, Autonomous and Deep Learning systems, where camera sensors can be confused by challenging lighting conditions.

These variables enable engineers to vastly scale their experiments, running multiple scenarios with changing weather conditions, time of year and time of day.

Third party conversion tools

The weather can severely impact the performance of ADAS and autonomous systems so it is important to thoroughly test this in a safe, controlled environment. In rFpro there is no need to wait for the right weather to come or try to artificially create it, the weather and atmospheric conditions can be either live-tuned or pre-programmed. Clouds, precipitation levels, snow, fog, intensity of light can all be varied and controlled.

The position of the sun and the moon is accurate to the latitude and longitude of the real location that is being replicated, so shadows and reflections are cast in the same way given a certain time of day. This makes it easier to more accurately replicate scenarios in the real world to correlate your results. This is particularly important for the testing and training of ADAS, Autonomous and Deep Learning systems, where camera sensors can be confused by challenging lighting conditions.

These variables enable engineers to vastly scale their experiments, running multiple scenarios with changing weather conditions, time of year and time of day.

IPG converter

The weather can severely impact the performance of ADAS and autonomous systems so it is important to thoroughly test this in a safe, controlled environment. In rFpro there is no need to wait for the right weather to come or try to artificially create it, the weather and atmospheric conditions can be either live-tuned or pre-programmed. Clouds, precipitation levels, snow, fog, intensity of light can all be varied and controlled.

The position of the sun and the moon is accurate to the latitude and longitude of the real location that is being replicated, so shadows and reflections are cast in the same way given a certain time of day. This makes it easier to more accurately replicate scenarios in the real world to correlate your results. This is particularly important for the testing and training of ADAS, Autonomous and Deep Learning systems, where camera sensors can be confused by challenging lighting conditions.

These variables enable engineers to vastly scale their experiments, running multiple scenarios with changing weather conditions, time of year and time of day.

Contact one of the team to learn more about how rFpro can support your vehicle development projects.

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