Safer, faster testing of ADAS and Autonomous systems
rFpro’s software is used to train, test and validate supervised learning autonomous driving models and ADAS systems.
AI learns through Training Data. Simulated training data sets allow AI to experience life threatening situations without risk to real road users.
Simulated testing allows rFpro users to push their AI to the limit, even in situations where the experiments are life threatening for passengers and other road users. rFpro experiments can scale across muliple simulations sharing the same virtual world, so tests can include real human road users too.
rFpro’s complete End-to-End, Physically Modelled, simulation means rFpro can be used for training AI, regression testing and identifying new failure modes, so rFpro customers’ simulation will be able to form part of future regulatory frameworks.
Full End-to-End testing also means rFpro users can involve their target customers – the passengers – in simulated testing. Involving passengers at the earliest possible stage helps ensure customer acceptance, protecting your investment in AI and autonomy.
how does rfpro deliver AUTONOMY
rFpro’s three steps to simulated testing:
1. physically model the real world
The 1st step means simulation according to the laws of Physics, abandoning the computationally efficient special effects that are adequate for games and cinematography, because we haven’t just got to convince humans, we’ve got to convince machine vision systems. Objects in rFpro’s Digital-Twins are made from materials that obey the laws of physics. When light strikes the paint on cars, the white lines on the road, the road surface, it must behave in the same way as the real world.
The atmosphere is also physically modelled, allowing experiments to run at different times of day, in different weather conditions. Again, because rFpro’s digital twins are physically modelled, when the rain is persistent, puddles will accumulate, in the correct places, and you may start to see reflections in the road surface.
By creating a physically modelled world, it means our simulation can correlate with the real world. If you want to be able to validate your simulation against the real world then you need to be physically modelling the real world. This will become essential as simulation starts to form part of the regulatory frameworks affecting AVs.