Menu
×
May 17, 2023 by John Makin and Gregor Matenaer
With the rapid evolution of technology, the automotive industry has entered a new era of innovation: The age of software-defined vehicles (SDVs). These cutting-edge vehicles redefine the traditional boundaries of the automotive world, as they have transformed from purely mechanical machines into sophisticated software systems on wheels. This paradigm shift is paving the way for a plethora of new features, such as advanced driver assistance systems (ADAS), autonomous driving capabilities, and enhanced connectivity — all of which are transforming the driving experience.
However, this transformation also necessitates a significant change in the way vehicle software is tested. Traditional testing methods are proving inadequate to deal with the complexities and nuances of modern SDVs. Consequently, the industry must adopt new testing strategies and methodologies to ensure the safety, reliability and performance of these next-generation vehicles. At the same time, there is a new opportunity to apply common process methods and tools for software development and validation for the automotive industry.
The integration of software and hardware
SDVs integrate software and hardware at a level previously unseen in the automotive industry. This amalgamation is central to the functioning of the vehicle, as it allows manufacturers to quickly introduce new features and functionalities while also providing seamless updates and improvements. However, this increased complexity also presents challenges when it comes to software testing.
Software heterogeneity
Software-defined vehicles are built on a diverse range of software platforms and components, each responsible for controlling specific aspects of the vehicle. This heterogeneity necessitates the development of advanced testing strategies that can effectively assess the interoperability of these software components.
Continuous integration and continuous deployment
In order to keep up with the rapid pace of software updates in SDVs, automotive companies are increasingly adopting Continuous Integration (CI) and Continuous Deployment (CD) methodologies. These practices require the implementation of robust testing processes that ensure the quality and reliability of the vehicle's software.
The test frameworks must be connected seamlessly to the CI/CD of the software integration. Aggregation from single SOC, to ECU, to overall vehicle have to be considered in a fully integrated collaboration model, that will cover all system levels. Pushing forward automation with virtualization and HIL testing at scale is key for a continuous homologation and testing. And user-in-the-loop tests need to be filed in the seamless process documentation as part of the overall agile product development cycle.
Security concerns
As SDVs become more connected and autonomous, they are also more susceptible to cyberattacks. Consequently, security testing has become an essential aspect of the software development lifecycle. Automotive companies must employ rigorous security testing methods to identify vulnerabilities and protect their vehicles from potential cyber threats.
Data-driven testing
The massive amount of data generated by SDVs has given rise to data-driven testing methodologies. These approaches leverage data analytics and machine learning to identify patterns and trends in vehicle behavior, ultimately allowing for more accurate and efficient testing.
Virtualization and automation at scale
Virtualization and automation at scale — with all simulation methods including digital twins — is the base for effective vehicle test platforms. Simulation on digital twins (vECUs/SILs) is increasingly being used in the testing of SDVs. These virtual environments provide a safe and cost-effective way to assess the performance of vehicle software under various conditions and scenarios without the need for physical testing. This creates a clear time advantage through hardware decoupling, especially as there’s no blocking/stopping of the development and validation process due to shortages in hardware sample availability.
Other digital twins — such as virtualized test data for AV/ADAS or interior sensor systems — can speed up testing and reduce effort drastically. Similarly, test bench and HIL hosting will become more important for the last mile. Combining SIL and HIL servers in a professional environment at scale, with 24/7 remote access, will define the standard.
Model-based testing
Model-based testing is another emerging methodology that is gaining traction in the automotive industry. It involves the creation of abstract models of the vehicle's software components. These are then used to generate test cases and evaluate the system's behavior.
Artificial intelligence and machine learning
AI and machine learning techniques are being employed to enhance the testing process for SDVs on all levels. These technologies can help in managing complexity, identifying potential issues and optimizing the testing process: This leads to more efficient and effective testing strategies. When should AI be used? Starting from the early stages in software verification and unit testing, going up into all integration and system test levels, and ideally to classify a huge number of issues and defects on many levels.
Embracing the future of automotive testing
The development of software-defined vehicles is revolutionizing the automotive industry, offering unparalleled levels of connectivity, intelligence and autonomy. However, this transformation also means we need a shift in the way vehicle software is tested. By adopting new testing strategies and methodologies — such as virtualization, simulation, digital twins, and data-driven testing — automotive companies can ensure the safety, reliability and performance of their next-generation vehicles while embracing the opportunities presented by this technological revolution.
John Makin
Gregor Matenaer
