Redefining traditional norms, the car is transforming from an internal combustion-powered, mechanical means of transportation to a software-defined vehicle (SDV). But how is the automotive sector realizing the transformation towards software-centric vehicles? To answer these questions and more, we conducted a comprehensive SDV-focused survey in the summer of 2023 with 141 experts from original equipment manufacturers (OEMs) and automotive suppliers based in Germany, France, and the United Kingdom (UK). 

The results of the study highlight some of the key issues the industry is facing today:

• What are the latest SDV trends?
• What strategies are companies embracing as the industry shifts to SDVs?
• What challenges do they have to overcome to achieve scale?
• What are the catalysts accelerating the SDV transition?

How will the SDV market develop?

The software-defined vehicle market is undergoing significant change, driven by technological advancements, changing consumer attitudes, innovative business models, regulatory developments, and a new focus on geographical markets. With such a wide variety of influences at play, the question arises as to when SDVs will become the norm. In our survey, 43% of respondents believe that widespread adoption of SDVs is achievable within the next five years, while 47% anticipate a timeframe of five to ten years (see fig. 1). 

Efficiency and speeding up time to market are key opportunities

The reduction in product lifecycles over the years is driving companies to adopt more flexible and dynamic practices in their transition to SDVs. When asked about the opportunities, the vast majority of respondents agreed that the SDV shift provides opportunities in terms of efficiency and speed of development (see fig. 2).

What are the most significant methodologies?

Efficiency is the name of the game in the SDV industry. To remain competitive, companies must embrace agile methodologies, that mirror the practices of technology companies. DevOps is emerging as a significant methodology, promising to boost the speed and quality of software development. When it comes to optimizing powertrains, SDVs provide the flexibility to customize components for optimal efficiency and performance. Predictive analytics, machine learning, and over-the-air (OTA) updates are the tools that enable SDVs to continuously adapt and improve their powertrains (see fig. 7).

Main barriers to the effective use of and ability to scale SDVs

Complexity and cost are the biggest hurdles for companies entering the SDV space (see fig. 10). Security and data privacy are also paramount, given the data-driven nature of SDVs. Political support and regulatory uncertainty add to the complexity, highlighting the need for clear guidelines and advocacy efforts. 

What are the technologies with the greatest impact on SDVs?

Reducing complexity through artificial intelligence (AI) and machine learning is seen as a promising solution. AI is driving the transformation with its data analysis capabilities. In addition, a high-performance computing (HPC) architecture is essential for processing vast amounts of data and ensuring real-time responsiveness (see fig. 16).

Download the full SDV study 2023 here and read all the results in detail.