About the Backgrounds and Motivations of the Software-Defined-Vehicle (SDV)

In addition to electromobility, automated/autonomous driving and vehicle connectivity, the so-called "Software-Defined Vehicle" (SDV) is one of the main fields of action in the transformation of the mobility industry. Among other things, it is currently being intensively discussed on social media (at the time of writing in autumn 2025), especially in the context of the competitiveness of the European automotive industry (compare, for example, the Autopreneur or the German business press).

With this article, we want to summarize the background and motivations from our point of view and our experience. After all, software is not a purpose in itself, but rather a means of implementing modern vehicle functions and increasing effectiveness in vehicle development. There are several principles behind the software-defined vehicle that massively favor, if not even make it possible, to master the complexity of current and future vehicles, including cooperative, connected, automated driving.

The main motivation behind the SDV is the increased functionality of the vehicles. While this seems obvious in the case of infotainment, this is especially true for the primary driving functions of the powertrain, the chassis and the safety functions. Their modern functionality would no longer be possible without software-based, digital control systems.

If modern control engineering and artificial intelligence methods are used here, functionalities can be implemented to the edge of physical/mechanical feasibility.

Topics such as automated/autonomous and connected driving are inconceivable without a focus on software and can usually only be mapped with appropriate algorithms and digital data processing.

In the case of software, improvements and adaptations can be implemented and deployed much more easily and quickly than when hardware components are affected. This makes SDVs better suited to solve complex problems incrementally through incremental, iterative improvements. Of course, this applies in particular to autonomous/automated and connected driving as well as to traffic automation in general.

This allows you to start with an initial set of functions, which still provides small but still existing added value, and then expand it step by step. In this case, the operational design domain of the function is constantly expanded or the function is trained and continuously improved from data in real operation. The implementation risk of complex functions remains manageable and under control, or insufficient functionalities can be improved more quickly.