Current developments in automotive industry such as hybrid powertrains and the continuously increasing demands on emission control systems, are pushing complexity still further. Validation of such systems lead to a huge amount of test cases and hence extreme testing efforts on the road. At the same time the pressure to reduce costs and minimize development time is creating challenging boundaries on development teams.
Therefore, it is of utmost importance to utilize testing and validation prototypes in the most efficient way. It is necessary to apply high levels of instrumentation and collect as much data as possible. And a streamlined data pipeline allows the fleet managers to get new insights from the raw data and control the validation vehicles as well as the development team in the most efficient way.
In this paper we will demonstrate a data-driven approach for validation testing. Managing the requirements and deriving the test cases is essential to continuously monitor the testing progress. Digitalization of the vehicle meta data as well as the driver feedback and combining this information with the measurement data enables new ways of analytics. Latest technologies in advanced data science can be applied to provide a new level of automation.
Digitalization of the validation process and advanced data analytics can provide great benefits such as reduction of prototypes or reduced testing time of fleets. But also lead to improved product quality due to verified test case coverage.