MARCH 2019CIOAPPLICATIONSEUROPE.COM9driving or AI-powered user experiences. In addition to in-vehicle supercomputing hardware, massively parallel processing solutions can power the data centers used to solve critical challenges faced in the development of safe AVs. A single test vehicle can generate petabytes of data each year. Capturing, managing, and processing this massive amount of data for not just one car, but a fleet, requires this type of high-performance computing architecture and infrastructure.Using the Virtual World to Train Cars for the Real WorldSimulation presents a powerful solution to what has been an insurmountable obstacle: driving millions of miles and reacting to difficult scenarios. By tapping into the virtual world, car makers can safely and accurately test and validate autonomous driving hardware and software. A simulated test environment is more than just a virtual car on a virtual road. It takes model building as intensive as those for movies, and as detailed and accurate as the blueprints for the city roads and highways the car will eventually drive on. And not only does this world need to look realistic, it must also obey the laws of physics. It needs to be designed to virtually test any potential environment and driving situation, with the ability to ingest world, vehicle and traffic scenario models. A robust simulation solution includes "hardware-in-the-loop" testing with a digital feedback loop, allowing developers to test both autonomous driving software and the hardware platform it runs on. In addition to testing, simulation is also a vital tool for vehicle validation. To be truly safe, vehicles must be able to react correctly, reliably, and safely in every possible driving situation. Experts from TÜV SÜD, a German technical inspection association, estimate that each fully automated driving function comprises 100 million such situations. Given this, scalable verification and test methods are needed to cope with this enormous number of scenarios. Simulation is a method that makes these goals attainable. TÜV SÜD, NVIDIA, and Austrian drive system developer AVL are working together to validate and establish simulation for such a process.The path forward for both the technology and the business model is far from fixed. But certainly a few things are needed to succeed in the development of the future car: A highly scalable platform that can enable all levels of autonomous driving and a software platform that combines deep learning, sensor fusion, and surround vision to enable a safe driving experience. This unified architecture needs to extend from the data center to the vehicle and provide an end-to-end solution that will conform to national and international safety standards. It can be trained on a GPU-based server in the data center, then fully tested and validated in simulation before seamlessly deployed to run on the AI computer in the vehicle. And finally, safety must be the focus at every step--from designing to testing and, ultimately, deploying a self-driving vehicle on the road. Cloud-to-car computing and car development by creating a digital representation of the vehicle require a completely new look at today's IT infrastructureSerkan Arslan
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