The automotive industry is facing significant changes as the internal combustion engine is replaced by electrified powertrains and a shift towards autonomy. This has given car manufacturers the opportunity to redesign and redefine the entire automotive experience, from the tires up. However, this transition is not easy for an industry that has traditionally relied on heavy, fossil fuel-powered vehicles that are manually controlled through a dashboard of buttons and levers.

A new generation of car buyers, who prioritize personalization and connectivity over horsepower or external styling, have made the in-car experience a key factor in automotive differentiation. Consumers are looking for smarter, safer, cheaper, more sustainable vehicles that are more rewarding to own and can be easily connected and upgraded through over-the-air updates.

While electrification and autonomy continue to be major news in the industry, there is an even bigger development taking place behind the scenes: the move towards software-defined vehicles.

Software-defined vehicles (SDVs) offer the potential to revolutionize the automotive industry. By aggregating all of a car’s systems into a single, interconnected network controlled by code, rather than physical mechanics, every aspect of the vehicle can be upgraded, fixed, and personalized remotely through over-the-air updates.

This has the potential to greatly improve the in-car experience for consumers, while also providing new revenue opportunities for dealers and allowing car manufacturers, OEMs, and tier one suppliers to instantly fix issues, avoid expensive recalls, and deliver a safer, more efficient, and more enjoyable experience than their competitors.

With an SDV, it’s possible to imagine stepping into your car and being informed that its brakes have become 5% more efficient or its battery will now last 5% longer. We’re used to this level of frequent, incremental upgrades with our smartphones – it’s time to expect it from our vehicles as well.

Software-controlled self-driving vehicles (SDVs) have the potential to operate autonomously as long as they have the necessary sensors to understand and navigate their environment. These sensors, which include cameras, radar, lidar, gyroscopes, and GPS, provide a wealth of data that can be used to train artificial intelligence (AI) models to drive autonomously. Currently, advanced driver assistance systems (ADAS), such as lane assist and adaptive cruise control, utilize these sensors to improve driving performance. However, even when not actively driving, the data generated by these sensors can be used to improve the decision-making capabilities of autonomous vehicles, helping them to navigate and respond to various scenarios and situations.

The success of software-defined vehicles depends heavily on the integration of a complex network of hardware components. However, the current demand for these components, coupled with disruptions in the electronics supply chain and limited manufacturing capacity, have made it difficult for Tier-1 subsystem suppliers to keep up. As a result, some of these suppliers are turning to in-house semiconductor design and automated design tools to optimize their systems and improve control over supply and inventory. While the use of older semiconductor manufacturing processes has helped to reduce costs in the past, the demands of high-value features in modern automotive electronics are driving the use of more advanced processes and newer process nodes. This is necessary to achieve the necessary performance, size, and power consumption for these features.