At CES 2018 (taking place until January 9 in Las Vegas, Nevada), Continental is showcasing a highly flexible computing platform for automated driving systems and processing of large amounts of data.

Developed at the company’s San Jose Research & Development Center in close collaboration with technology company Xilinx, the Assisted & Automated Driving Control Unit will enable customers to develop technologies quicker by building upon the Open Computing Language (OpenCL) framework.


The platform provides heterogeneous computing options such as a CPU, GPU, DSP, and now with the help of Xilinx’s all programmable technology, a customizable hardware acceleration solution. This enables developers to optimize software for the appropriate processing engine or to create their own hardware accelerators with the Xilinx all programmable technology.

The result is maximum freedom to optimize performance without sacrificing latency, power dissipation or the flexibility to move software algorithms between the integrated chips, as the project progresses.

“Xilinx is proud to have collaborated with Continental in the development of the Assisted & Automated Driving Control Unit, enabling the creation of an ecosystem for automated driving. We embrace the spirit of a hardware platform that invites collaboration, rather than tying companies to a proprietary architecture,” said Willard Tu, senior director, automotive market at Xilinx.

“Our Assisted & Automated Driving Control Unit will enable automotive engineers to create their own differentiated solutions for machine learning, and sensor fusion. Xilinx’s all programmable technology was chosen as it offers flexibility and scalability to address the ever-changing and new requirements along the way to fully automated self-driving cars,” noted Karl Haupt, head of Continental’s ADAS business unit.

“For Continental, the Assisted & Automated Driving Control Unit is a central element for implementing the required functional safety architecture and, at the same time, a host for the comprehensive environment model and driving functions needed for automated driving.”