Manage V&V strategies for highly automated driving systems
Many monumental challenges remain with autonomous vehicle development before autonomous vehicles become mainstream. Most automotive players have yet to overcome the complexity and size required for verification to release highly automated driving systems to the public. At the same time, data management challenges create a need for extensive integration of application lifecycle management (ALM), product lifecycle management (PLM), and simulation tools.
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Although simulation will soon become the predominant source of highly automated driving verification and validation strategies, there are still some drawbacks. While better coverage and increased verification confidence may come with more simulation test cases, it also means higher costs in computing infrastructure and workforce. And while a more extensive result database potentially comes with more development insight, it also adds more time to analyze the data without missing critical failure patterns. Siemens Simcenter Prescan360 addresses these challenges by providing the required verification and adequately combining:
Collaboration among an increasing number of contributors is necessary to advance highly automated driving systems development. However, this increases the risk of information not being shared, synchronized, or traced across departments. Keep track of ever-increasing complexity systems and enable collaboration of design, integration, and verification activities across numerous departments to maximize development productivity with Siemens Simcenter Prescan360. It also allows creating, importing, or generating scenarios, actors, and environments for sensor configuration in the usability domain. And with automated processes involving tools in any customer ecosystem, mistakes and rework can be avoided while accelerating verification loops.
Closed-loop autonomous vehicle development is necessary to manage the complexity of highly automated driving systems. The product lifecycle starts with capturing real-world data because it is a data-driven process using massive amounts of data for machine learning and statistical analysis. Next, the collected data is analyzed for extraction and prepared for data-driven design and performance evaluation. Once extracted, the new dataset supports design exploration and generative design optimization. Lastly, the verification and validation phase must prove the autonomous vehicle is safe and comfortable enough to deploy in the real world. Whether designing, exploring, verifying, or validating autonomous vehicle systems, Siemens Simcenter Prescan360 supports all four product lifecycle stages with an integrated tool suite and services.
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