Businesses can unlock immense opportunities and benefits by embracing software-defined vehicle architectures. Here are 7 of the most important SDV solutions of our times.
1. OTA (Over-The-Air) Updates
Most people know what OTA updates are because they receive them on smartphones, laptops, digital cameras, and other connected devices. In an SDV context, OTA software updates ensure the consistent and iterative improvement of vehicular safety and performance features. Drivers can receive and respond to OTA updates from their cockpit, much like how we update operating systems on personal devices.
2. Virtualization
Using technologies such as digital twins and extended reality (XR), virtualization allows companies to affordably design, develop, prototype, and validate new physical and virtual SDV and digital cockpit components without making hardware investments or getting under the hood of an actual vehicle.
3. Edge Computing
In the realm of software-defined vehicles (SDVs), edge computing emerges as a pivotal technology, enhancing the capabilities and efficiency of autonomous systems. By processing data closer to its source—right at the vehicle itself—edge computing drastically reduces latency and improves response times. This immediate data processing is crucial for real-time decision-making in autonomous driving, where every millisecond counts. It enables vehicles to analyze and act upon critical information locally, from traffic conditions to sensor data for obstacle avoidance, without the need for constant cloud connectivity. This not only streamlines the operation of SDVs but also bolsters their safety features, ensuring that decisions are made swiftly and reliably, even in areas with poor connectivity. Furthermore, edge computing supports advanced features such as real-time analytics, local data caching, and immediate content delivery, enhancing the overall driving experience and vehicle performance.
4. Cloud Connectivity
Autonomous vehicle companies use myriad cloud services as pillars for their software-defined vehicle architecture. Most companies are moving from on-premises data centers to cloud-based platforms, which means cloud connectivity is one of the most important attributes and technologies associated with SDVs. Seamless performance across digital cockpits of connected vehicles and backend infrastructures is only possible with cloud technology.
4. AI
AI is one of the central protagonists in the story of SDV solutions. Connected vehicles rely on real-time sensor data analyses and data from other disparate external sources. AI mechanisms analyze data at subsecond speeds to report information to the vehicle’s cockpit. For example, if a camera in the car catches the driver not paying attention to the road, AI mechanisms can immediately sound an alert.
5. IoT (Internet-of-Things)
In some ways, connected vehicles are IoT devices. But connected devices are also full of smaller IoT technologies. For instance, all mechanisms that either deliver or receive data to or from digital cockpits are IoT devices. IoT technologies in autonomous vehicles include smart cameras, instrument clusters, steering wheels, infotainment dashboards, and heads-up displays (HUDs). Without these IoT technologies, an SDV isn’t an SDV.
6. 5G
A digital cockpit and its instrument clusters form an all-in-one console for drivers. It melds the worlds of entertainment, security, safety, and performance into a unified real-time platform. Any unified, real-time platform requires immense speeds and minimal latency for data transfers. 5G connectivity is essential to running real-time applications and technologies in digital cockpits. Without 5G, digital cockpits may lag, and a suboptimal digital cockpit is a risk in itself.
7. Predictive Maintenance
Numerous sensors in connected vehicles collect vast amounts of data. AI/ML tools then process and analyze that data to generate actionable insights. A lot of this information is accessible to drivers in their digital cockpits. Amongst these insights are invaluable predictions about vehicle health and maintenance needs. While certain IoT sensors on cars provide details on mechanical maintenance needs, a software-defined vehicle architecture calls for another kind of predictive maintenance, one that involves warnings and updates about software improvements, cybersecurity vulnerabilities, and bugs.