Autotechreview

Continental Puts NVIDIA-Powered Supercomputer For Vehicle AI Into Operation

Continental NVIDIA-Powered Supercomputer Vehicle AI Operation Autonomous Driving ADAS NVIDIA DGX
{data-social}

Continental and NVIDIA are building a high-performance cluster set to boost autonomous driving development performance that reduces development time from weeks to hours

Continental has invested in setting up its own supercomputer for Artificial Intelligence (AI), powered by NVIDIA InfiniBand-connected DGX systems to develop innovative technologies around Autonomous Driving even more efficiently and quickly. It has been operating from a data centre in Frankfurt am Main, Germany, since the beginning of 2020, and is offering computing power as well as storage to developers in locations worldwide. AI enhances advanced driver assistance systems, makes mobility smarter and safer, and accelerates the development of systems for autonomous driving, the company noted.

Continental’s supercomputer is built with over 50 NVIDIA DGX systems, connected with the NVIDIA Mellanox InfiniBand network. It is ranked as the top system in the automotive industry, according to the publicly available list of TOP500 supercomputers. A hybrid approach has been chosen to be able to extend capacity and storage through cloud solutions if needed, the company said. The supercomputer has every detail planned precisely in order to ensure the full performance and functionality today, with scalability for future extensions, it added.

Advanced driver assistance systems (ADAS) use AI to make decisions, assist the driver and ultimately operate autonomously. Environmental sensors like radar and cameras deliver raw data. This raw data is processed in real-time by intelligent systems to create a comprehensive model of the vehicle’s surroundings and devise a strategy on how to interact with the environment. Finally, the vehicle needs to be controlled to behave like planned. But with systems becoming more and more complex, traditional software development methods and machine learning methods have reached their limit. Continental noted that Deep Learning and simulations have become fundamental methods in the development of AI-based solutions.

In the case of 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. Continental said that a child is capable of recognizing a car after being shown a few dozen pictures of different car types. However, several thousand hours of training with millions of images and 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 NVIDIA DGX POD not only reduces the time needed for this complex process, it also reduces the time to market for new technologies, it explained.

To date, the data used for training those neural networks comes mainly from the Continental test vehicle fleet, the company said. It added that currently, they drive about 15,000 test kilometres each day, collecting around 100 terabytes of data. 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, it explained.

The advantages of this process come in the form of firstly making recording, storing and mining the data generated by the physical fleet unnecessary over the long run, as 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, this will allow vehicles to navigate safely through changing and extreme weather conditions or make reliable forecasts of pedestrian movements – thus paving the way to higher levels of automation.

Christian Schumacher, Head, Programme Management Systems, Advanced Driver Assistance Systems business unit, Continental, “The supercomputer is an investment in our future.” The state-of-the-art system reduces the time to train neural networks, as it allows for at least 14 times more experiments to be run at the same time, he added. Schumacher noted that Continental selected NVIDIA after intensive testing and scouting, and the project was implemented in less than a year.

NVIDIA DGX systems give innovators like Continental AI supercomputing in a cost-effective, enterprise-ready solution that’s easy to deploy, observed Manuvir Das, Head, Enterprise Computing, NVIDIA. He said Continental is engineering tomorrow’s most intelligent vehicles, as well as the IT infrastructure used to design them by utilising the InfiniBand-connected NVIDIA DGX POD for autonomous vehicle training.

“Overall, we are estimating the time needed to fully train a neural network to be reduced from weeks to hours,” said Balázs Lóránd, Head, AI Competence Centre, Continental, who also works on the development of infrastructure for AI-based innovations together with his groups in Continental. Lóránd added that the development team has been growing in numbers and experience over the past years. With the supercomputer, the team is now able to scale computing power even better according to its needs and leverage the full potential of the developers, he noted.