Automotive Supercomputer for Automated Driving.
Advanced driver assistance systems use AI to enable a vehicle to make decisions, assist the driver and ultimately operate autonomously. But with systems becoming more and more complex, traditional software development methods and machine learning methods have reached their limit. Deep Learning and simulations have therefore become fundamental methods in the development of reliable and robust AI-based solutions.
Deep Learning
With Deep Learning, an artificial neural network enables the machine to learn by experience and connect new information with existing knowledge, essentially imitating the learning process within the human brain.
But while a child is capable of recognizing a car after being shown a few dozen pictures of different car types, several thousand hours of training with millions of images and therefore enormous amounts of data are necessary to train a neural network that will later on assist a driver or even operate a vehicle autonomously.
The supercomputer not only reduces the time needed for this complex process, it also reduces the time to market for new technologies. The time needed to fully train a neural network can therefore be reduced from weeks to days.
Simulation and Virtual Data Generation
To date, the data used for training those neural networks comes mainly from the Continental test vehicle fleet. Currently, they drive around 15,000 test kilometers each day, collecting around 100 terabytes of data – equivalent to 50,000 hours of movies.
Already, the recorded data can be used to train new systems by being replayed and thus simulating physical test drives. With the supercomputer, data can now be generated synthetically, a highly computing power consuming use case that allows systems to learn from travelling virtually through a simulated environment.
Advantages for the development process
Firstly, over the long run, it might make recording, storing and mining the data generated by the physical fleet unnecessary, as all necessary training scenarios can be created instantly on the system itself.
Secondly, it increases speed, as virtual vehicles can travel the same number of test kilometers in a few hours that would take a real car several weeks.
Thirdly, the synthetic generation of data makes it possible for systems to process and react to changing and unpredictable situations, ultimately allowing vehicles to navigate safely through changing and extreme weather conditions or make safe prognoses on pedestrian movements – thus paving the way to higher levels of automation.
The supercomputer is powered by NVIDIA
Continental’s supercomputer is built with more than 50 NVIDIA DGX systems, connected with the NVIDIA Mellanox InfiniBand network. It is ranked according to the publicly available list of TOP500 supercomputers as the top system in the automotive industry. A hybrid approach has been chosen to be able to extend capacity and storage through cloud solutions if needed.
Press Release: Continental puts its own supercomputer for vehicle AI system training, powered by NVIDIA DGX, into operation