DSPACE ACHIEVES THE AWS AUTOMOTIVE COMPETENCY DESIGNATION
Showcase at IAA Mobility demonstrated how cloud-based solutions shorten the development times of ADAS and AD applications.
dSPACE announced that it has achieved Amazon Web Services (AWS) Automotive Competency status. This designation recognizes dSPACE for its expertise in providing customers with professional services and/or software solutions delivering transformation across an automotive company’s operating model.
More and more, automotive customers leverage the cloud to innovate and transform their operating model, relying on cloud experts with extensive automotive experience. AWS Automotive Competency Partners provide customers with solutions and services across their digital transformation journey while being assured they have support from a validated AWS Partner to meet their needs. These solutions follow AWS’s best practices for building secure, high-performing, resilient, and efficient cloud infrastructure for industry-specific applications.
Achieving the AWS Automotive Competency defines dSPACE as an AWS Partner Network (APN) member with demonstrated technical proficiency and proven customer success in running cloud solutions on AWS for the automotive industry. This program showcases automotive consulting and software AWS Partners who have domain knowledge and provide cloud services. To receive the AWS Automotive Competency designation, AWS Partners must undergo rigorous technical validation and provide vetted customer references.
At the IAA Mobility in Munich, Germany, dSPACE and Equinix demonstrated how cloud-based solutions for the automotive industry can shorten the development times of ADAS and AD applications.
The showcase demonstrated how data recorded from a test vehicle with a dSPACE Autera Data Logging System is uploaded to AWS via 100G Direct Connect located in an Equinix data centre. Once the data sets are available and curated in the cloud, they can be downloaded to dSPACE data replay stations in Equinix data centres around the world to validate perception and data fusion algorithms of ADAS/AV ECUs under real-world conditions.