For years, forward-looking people in the automotive industry have focused on the development of self-driving vehicles — and for good reason. Autonomous driving (AD) vehicles, as they are known in the industry, hold the promise of safer, more comfortable and more efficient transportation for all of us.
Unlike road-weary drivers, AD vehicles have no trouble staying alert behind the wheel, even in the darkest hours of night. They can be trained to not only drive the vehicle under normal conditions, but to recognize and react to unlikely scenarios in the roadway. And they will make for a much more pleasant mobility experience for drivers, who can use their time in transit for more interesting activities than staring at the road ahead and the vehicles in it. AD vehicles will even bring us more efficient roadways, as they coordinate their actions with each other and with traffic management systems.
That’s all the easy part. The hard part is getting there. While the destination is clear, the road to self-driving vehicles comes with some big technical barriers that have to be overcome before we enter this brave new world of vehicles that do the driving for us.
The barriers in the road to AD
The road to self-driving vehicles is like a cross-country trip with important milestones along the way, each of which brings us closer to the ultimate destination. These milestones, established by the Society of Automotive Engineers, include two levels of driver support focused on steering and/or braking and accelerating assistance, two levels in which the car drives itself under limited conditions, and the ultimate level in which the car drives itself everywhere under all conditions.
Each of the milestones in the road to AD vehicles brings its own set of challenges. As each new level is reached, moving to the next requires extensive development, test driving and scenario development, with corresponding data processing and storage requirements growing exponentially at each level. In fact, the highest level of AD will likely require collecting, processing and analyzing exabytes of data. This reality makes data the defining characteristic of AD development — in terms of both the sheer volume and the uncertainty about its growth. Success in this realm is all about innovating with data.
To handle the large data volumes, high performance computing (HPC) systems, supported by artificial intelligence (AI) solutions, must provide high throughput to power many parallel streams of data analysis, simulation and correlation to deliver high‑end simulations. This means that automotive manufacturers and suppliers on the road to AD need to roll out IT infrastructure capable of supporting these steep requirements at every level of the ADAS/AD hierarchy.
And this is where things get even harder. The IT infrastructure needed to support ADAS and AD development is both large and complex, often consisting of thousands of servers and several software stacks. There is also no one-size-fits-all approach for ADAS and AD architectures because each car manufacturer or parts supplier has its own requirements, approach and development roadmap.
That said, there are some universal requirements here. The infrastructure needs to be performant, efficient, cost‑effective and robust enough to span the development cycle for one level, and scalable enough to span ongoing development levels. All the while, the IT architecture should have the flexibility to incorporate new hardware and software as new insights emerge, new tools are developed, and new regulations come into place.
How Dell Technologies helps
To meet these steep requirements, and to overcome the enormous complexities of designing and building AD vehicles, manufacturers need to work closely with technology partners who have the breadth of products and technical expertise to cover the diverse requirements of designing and building self-driving vehicles.
Dell Technologies covers the entire ADAS/AD development chain, and can provide supporting high performance computing and artificial intelligence solutions both for car manufacturers that own the complete chain and for organizations that focus on a subset of components in the chain. Dell Technologies has decades of experience designing cost‑effective systems for HPC and AI, and delivering them in a simplified and customizable building block models.
Dell Technologies also has the partnerships necessary to deliver AD/ADAS solutions with the latest technologies. For example, Dell Technologies and NVIDIA work together closely to deliver AI, HPC and data analytics solutions for data- and compute-intensive challenges. In the case of Dell Technologies solutions for ADAS and AD, multiple AI deep neural networks and algorithms for computer vision, localization and path planning run on a combination of integrated NVIDIA GPUs, CPUs, deep learning accelerators and programmable vision accelerators. Solutions like these wouldn’t be possible without close partnerships among technology leaders.
For a deeper dive into this topic, see the Dell Technologies white paper “The ADAS/AD Architecture,” published as part of the Dell Technologies HPC & AI Innovation Exchange series. This paper provides an in‑depth technical analysis of a range of solutions for manufacturing and automotive companies working to develop ADAS and AD vehicles and their components. It also dives down into various options available for specific use cases and workloads, including remote site and data center infrastructure, software, services, and infrastructure design.