Tool chain for automated and autonomous driving functions

Partnership between AVL, Microsoft, Hexagon, Synopsys and Tracetronic Tool chain for automated and autonomous driving functions

2024-04-04 Source: Press release | Translated by AI 1 min Reading Time

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Together with Microsoft, Hexagon, Synopsys and Tracetronic, AVL is developing a comprehensive digital and automated toolchain for automated and autonomous driving functions. This should enable efficient and precise simulation of the functions as well as their testing in connection with various sensors, control elements and driving environments. The aim is to increase the number of tests.

In order to develop a digital and automated tool chain, AVL, Microsoft, Hexagon, Synopsys and Tracetronic have teamed up.(Image: AVL) — In order to develop a digital and automated tool chain, AVL, Microsoft, Hexagon, Synopsys and Tracetronic have teamed up.
(Image: AVL)

The testing of automated and autonomous vehicles presents major challenges for the automotive industry. Complex functions must withstand different real driving scenarios and environmental conditions. This involves simulating and testing the interaction of sensors, control elements and the vehicle environment. To drive the development of new functions and their integration, testing procedures must be automated at each stage of development and interlock optimally - from software in the loop to hardware in the loop to real road tests.

Initiative ADET Autonomous End-to-End-Testing

The partners have launched the ADET Autonomous End-to-End Testing initiative to develop a seamless, digital and automated tool chain. This allows the number of simulation runs to be increased by a factor of 500 while maintaining the same development time.

Easy data exchange between test scenarios and teams.

With the digital and automated test workflows and tools, companies are promoting the effective creation and exchange of test scenarios. Manufacturer and supplier teams can access the results regardless of location and time. Any system malfunctions are detected and removed at an early stage of development. The tests can be planned efficiently and simulations can be quickly implemented on the Microsoft Azure infrastructure. Evaluations and analyses of data and results can be automated and exchanged seamlessly. Processes, tools and test procedures are orchestrated from a central location. This architecture will include services such as Azure Kubernetes Service (AKS) for container orchestration, Azure Batch for automatic scaling, and Azure's specially developed HPC/AI compute instances. AI workloads, including machine learning operations (MLOps), will be supported by Azure Machine Learning. (se)

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