Machines need photorealistic… machine data
What does this mean?
Our human perception system has already evolved to be as pixel-accurate as possible, but machines perceive differently, possibly more accurately than humans do.
And how is this linked to the autonomous driving use case?
The autonomous driving market demands data with the highest level of accuracy
The advanced perception and AV/ADAS industry is implementing a new generation of sensors and optical systems (such as, new photodetectors in different parts of the spectrum, and other advanced optical and sensory capabilities) that perceive the world in very different ways than humans do and demand data capable of fulfilling these new functionalities.
Safety first: no room for uncertainty
Now that we understand why data accuracy is key to a successful perception systems & new-generation sensors combination, it’s time to emphasize another important matter. Data accuracy has nothing to do with the concept of photorealism that we commonly attach to the images generated by off-the-shelf, real-time computer graphics engines.
Don’t get me wrong, these engines can develop beautiful flashy images that can perfectly fit the requirements for other and less complex applications, but they don’t provide the precision for accomplishing the required data accuracy to develop human-safe and trustworthy, fully autonomous transport, based on artificial perception.
Let’s take the aerodynamics sector as an example to better visualize the problem.
Data accuracy has always been a sensitive matter for aerodynamics engineers. For decades, they have only trusted the wind tunnel as the closest-to-real data source, and it was only a few years ago when simulation (or synthetic data) was introduced to their data generation pipelines.
Perhaps in the aerodynamics of a building, a certain margin of error can be tolerated, but in such a critical case as an airplane wing designing process, high data accuracy is vital (a thousandth of an error could imply a waste of tons of fuel).
Not all data is valid to train human-safe autonomous vehicles
Directly connected to human safety, autonomous driving is another critical use case. Safety becomes one sine qua non condition AV/ADAS developers must commit to if they don’t want to end up working in a quicksand paradigm… and this means a model shift from photorealistic data (according to human perception) to pixel-accurate data according to their autonomous vehicle sensor definition, optical system, and underlying AI.
Anyverse™ helps you continuously improve your deep learning perception models to reduce your system’s time to market applying new software 2.0 processes. Our synthetic data production platform allows us to provide high-fidelity accurate and balanced datasets. Along with a data-driven iterative process, we can help you reach the required model performance.
With Anyverse™, you can accurately simulate any camera sensor and help you decide which one will perform better with your perception system. No more complex and expensive experiments with real devices, thanks to our state-of-the-art photometric pipeline.