Accelerating insight into vehicle safety at Continental

For most of us, driving is second nature; a series of automatic decisions. To train AI for autonomous driving—to make those same decisions even a 10th of a second faster, and potentially make driving safer—requires petabytes of data.

According to the World Health Organization (WHO) (link resides outside of ibm.com), approximately 1.35 million people die in road accidents every year, and another 50 million are injured. To mitigate this risk, the EU now requires the availability of self-driving vehicles by 2030. The race is on to provide the best technological path to fully autonomous driving.

“Advanced driver assistance systems react faster than drivers in critical accident scenarios,” says Robert Thiel, Head of Artificial Intelligence, Advanced Driver Assistance Systems (ADAS), at Continental. “This can be achieved by training an AI with tons of data to drive more safely than a human. Therefore, smart data management means smart vehicles and saved lives.”

Continental is a major supplier of automotive parts to nearly every car producer in the world and a leader in the autonomous driving intelligence space. Its ADAS business unit began developing intelligent sensors and data-driven traffic safety solutions more than 20 years ago, and has been working to increase the speed of development using deep learning and training artificial neural networks. The goal of Continental’s Vision Zero project is to virtually eliminate deaths by traffic accident through the development of ADAS technologies.

Continental’s ADAS solutions can support drivers in many typical driving tasks and even take control of the vehicle to avoid an accident. But as automation of its driver assistance and vehicle safety systems increased, software complexity grew, as did the number of safety requirements across multiple geographies. Continental realized it was time to scale both its technology and its teams to evolve a more globally scalable AI solution. The need for parallel data access also meant facing down a growing data management challenge.

Continental needed a powerful parallel file system to meet the high-speed demands of AI and to protect sensitive data. At the same time, it had to create a more centrally accessible place to store data and improve traceability, offering many ways for developers to securely connect.

Continental knew it was time to boost performance with scalable deep learning infrastructure and storage connected with a high-speed network. This solution would need to provide fast random access, support protocols such as Server Message Block (SMB) and Amazon Simple Storage Service (S3), and provide several different access management options.

“GPUs today are so fast that standard storage cannot keep up with the compute,” says David Enenkel, Head of IT Operations, ADAS, at Continental. “That’s why we were looking for something faster, something that really gives us the bandwidth and also the random access that we need.”

Continental established comprehensive testing and evaluated how well each of the top storage solutions measured up to its goals. To measure the performance of IBM ESS, Continental worked with IBM Business Partner SVA System Vertrieb Alexander GmbH. They found that the IBM IBM Storage for Data and AI with NVIDIA DGX solution provided the required performance and several other advantages. The “parallel” high performance architecture and simple-to-scale node deployment of the solution provided the AI infrastructure required, with the resilience and scalability Continental will need in the future.

The flexibility and seamless integration of IBM Storage with Kubernetes containers allowed Continental to modernize its application development without giving up on infrastructure requirements like performance, scalability or simplicity. The IBM Storage Scale solution ensured that its infrastructure will support the growth required, whether in the cloud or on premises. IBM has extensive experience in the automotive industry, which was also a contributing factor in Continental’s decision.

With the new solution, Continental was able to optimize for deep learning with multi-node training, enabling it to increase model accuracy for higher levels of safety without impacting time to production. Continental scaled to a cluster of DGX to handle 14 times more experiments per month, with the ability to test millions of permutations in environmental conditions—such as rain, snow, sunlight and clouds—or transients—such as cars moving too close to one another during a lane change.

With the performance improvements, flexibility and scalability of the new IBM data management solution to support an evolving AI infrastructure, Continental is on the fast track to change the future of mobility.