A modern vehicle, be it a car, train, or airplane, is a complex SW system where thousands, even millions, of signals interact and exchange instant communication. For instance, a modern car has over 100 ECUs, with an ever-increasing computational power, producing over 10 million interconnections. This complexity makes it extremely difficult to efficiently develop and upgrade these systems, maintain the necessary documentation, and comply with legislative requirements. This discussion presents how a digital twin at signal level can effectively address this problem by clearly exposing interdependencies rooted in the system’s source code, while maximizing automation
In this session, you will get a better understanding about:
- How to master software complexity, by compiling a digital twin at signal level which allows you to filter, visualize and extract your data.
- How this approach is highly scalable, flexible and automatable, making it ideal for modern, segmented software development scenarios.
- How to understand the impact of software changes and correct inconsistencies at an early stage, thereby saving time and money.
- How to enhance and ensure the quality of your “Over-the-Air” updates, by detecting and correcting critical issues before deployment.
- How applying AI to the dependency nets allows you to identify anomalies and vulnerabilities in your system, as well as define your testing strategy.
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