What is a Software-Defined Vehicle?

The automotive industry is undergoing a digital shift, with software-defined vehicles (SDVs) representing the very peak of data-driven innovation. SDVs are seen as the next generation of advanced vehicle systems, representing an ongoing transformation in automotive innovation as cars become less hardware-centric and more software-centric. There is a strong focus on the in-car user experience, augmented by technology such as infotainment systems.

SDVs still require a higher level of maturity before they see widespread use, but they offer distinct advantages over today’s vehicles. Instead of being limited to features installed at the time of manufacturing, SDVs can continuously upgrade their software features and functions once on the road. This ability to fix problems and make updates is driving SVD development, ultimately leading to a better user experience.

As of today, we have yet to see any full SDVs in existence. Like autonomous vehicles, SDVs can be categorized into levels 0-5, in which Level 0 is “software enabled” and Level 5 is a fully software-defined vehicle. While the industry is making progress toward Level 3 and Level 4 SDVs, it will be some time before Level 5 SDVs take to the road. Like with autonomous vehicles, not all original equipment manufacturers (OEMs) may aim to reach Level 5, so many Level 3 and Level 4 vehicles may exist in the future alongside Level 5 SDVs.

SDVs differ from other vehicle architectures due to their deep software integration, such as in connected vehicles and autonomous vehicles (AVs). SDVs focus on the in-vehicle functions, driving experience, and user experience inside the vehicle. In contrast, other advanced vehicles often focus their advanced software on communication features and environmental interaction.

But SDVs offer more than just in-car functions. They also provide advanced software-driven safety features, such as anti-collision systems and advanced driver-assistance systems (ADAS). Vehicles traditionally use a distributed architecture, in which electronic control units (ECUs) with their own software component are installed to carry out a single function, such as controlling a window. SDVs aim to use fewer ECUs by adopting a zonal architecture that’s fully centralized in the software, enabling shared hardware resources and reducing interference.

This means that an SDV can have fewer but more powerful ECUs that are connected to each other to perform multiple tasks, such as running both ADAS and infotainment applications on the same hardware. It also enables more flexibility for software updates and upgrades targeted to specific zones within the vehicle.

Decoupling the software from the hardware also enables SDVs to achieve higher performance and offer a wider range of in-car functionalities when compared to other vehicles. This also brings several other unique benefits, including:

• Flexibility
• Easy upgrading
• Improved safety and reliability
• Improved development efficiency
• Additional revenue streams

One of the unique features of SDVs is their ability to do over-the-air updates (OTA). OTAs enable manufacturers to install new vehicle features, functions, and updates into the vehicle’s software using the cloud, similar to how smartphones update using wireless technology. With this feature, vehicle technology can remain up to date over time and integrate with features that may not have been available during manufacturing.

SDVs contain many components. Some are what we would traditionally expect to see in vehicles, but others, not so much. There are three critical component classes within SDVs:

• Hardware
• Software
• Interfaces between the hardware and software

Hardware

Even though software is often the most discussed aspect of SDVs, a critical hardware layer supports the data collection and processing capabilities of those advanced software applications. Some key hardware components include:

• Centralized computing platforms
• Sensors: cameras and radar, lidar, and collision sensors
• Actuators
• ECUs
• Powertrain
• Chassis
• Suspension

In SDVs, sensors monitor both internal and external environments, enabling the software to make informed decisions. ECUs manage the vehicle’s electrical systems, and higher-performance ECUs can manage multiple systems simultaneously. Actuators execute different commands from the ECUs, such as braking and steering.

Because SDVs operate like data centers on wheels, high-performance computing (HPC) systems and powerful graphics processing units (GPUs) are needed to process sensor data and run applications like ADAS efficiently. Because SDVs are more connected than traditional vehicles, they must utilize communication networks like Ethernet to support faster data transfer capabilities.

Software

The software layer of an SDV includes core operating systems such as Windows or Linux to ensure internal connectivity with data communication technologies. OTA communication systems connect with external data centers to transfer information, and they provide the basis for updating the onboard automotive software and adding new features and functions to the vehicle.

SDVs also contain a range of app and user experience software, such as infotainment systems, digital cockpits, ADAS, advanced vehicle management controls, adaptive cruise control, internal climate control, and navigation systems. These connect to the SDV’s operating system via middleware, which acts as a software layer that enables communication between the operating system and individual applications. These features provide greater comfort and safety when driving.

SDVs continuously monitor their own performance and plan maintenance schedules through their data generation and analysis capabilities. Predictive maintenance enables vehicle operators to fix any issues before they become a larger problem.

Interfaces Between Hardware and Software

Between the hardware and software layers lies a middle layer known as the interface layer, which connects software programs with the SDV’s hardware. This interconnection requires app standardization across OEMs and suppliers to ensure compatibility.

Software in SDVs is integrated using a modular approach, in which each application has its own application programming interface (API). Similarly, SDV hardware is also modular, as smaller control units perform specialized tasks for different features. This enables designers to incorporate larger, higher-performance systems into the SDV using smaller “building blocks,” depending on the intended function.

While advances in software development drive the application side of SDVs, interface layers are crucial for organizing the hardware and software systems into a functional ecosystem that can monitor and act in real time. These interfaces are also critical for both vehicle safety and user experience.

The development of SDVs not only impacts the automotive industry, but the technology developed will be useful for other industries. Furthermore, SDVs themselves will have many use cases that will benefit other sectors.

For example, SDVs must have powerful CPUs and GPUs to handle the large amount of data produced. As a result, computer hardware manufacturers have needed to develop more advanced CPUs, 5G systems, and edge computing systems to support these and other applications.

In the automotive industry, SDV development is enabling more advanced ADAS technology that supports other autonomous driving applications. Additionally, the amount of data produced by SDVs provides more accurate real-time updates in commercial vehicles and fleet management applications. SDVs could also help to grow the mobility-as-a-service (MaaS) industry by providing users with on-demand access to a range of vehicles and personalize the MaaS experience to meet their needs.

Because SDVs can provide detailed data on driving behavior and vehicle performance, the technology also has the potential to revolutionize the vehicle insurance industry. This may lead to more personalized and dynamic insurance models for drivers.

SDVs will be able to connect with smart cities and smart grids, which use digital technology and data to improve things like public services and infrastructure for residents. These will use vehicle-to-grid (V2G) and grid-to-vehicle (G2V) operations to manage energy distribution in the grid. Managing SDV fleets within smart cities may help reduce traffic flow or accidents, and SDVs may also communicate updates in real time to city officials using OTA technology.

Like any vehicle architecture, SDVs offer both benefits and challenges. SDVs require a higher level of complexity due to their different software platforms and software architectures, and ensuring interoperability with both conventional and advanced hardware is key.

Some of the benefits of SDV technology include:

• Improved engine, battery, and fuel performance through advanced monitoring
• Improved safety through ADAS and anti-collision systems
• OTA software updates and the ability to add new features and functions
• Vehicle self-monitoring through telematics and diagnostics and the ability to perform predictive maintenance
• A personalized experience for each passenger in the car, from customized dashboards to infotainment choices
• Increased comfort for passengers through more infotainment services

However, SDVs are not a mature system. There are still several engineering and design challenges that designers and manufacturers must overcome:

• Complex software and large amounts of code are required, which increases the chance of bugs.
• As a digitally connected vehicle, SDVs are vulnerable to cybersecurity threats because they connect to other networks.
• SDVs collect a massive amount of data, which could lead to privacy concerns if data security best practices are not followed.
• The field is technically demanding and requires expertise from many areas of engineering, so talent and expertise gaps may be a problem as SDVs scale up to larger volumes.
• Manufacturing delays are already a problem with electric vehicles. Because SDVs have a complex integrated electronic and hardware environment, this may cause even greater delays.

Ensuring Cybersecurity in SDVs

Cybersecurity is a unique challenge to SDVs. As they exchange a lot of data both within the vehicle and to external data networks, there are many potential entry points and vulnerable nodes within the vehicle itself.

To protect against hacking attempts, more robust cybersecurity and secure communication protocols will be required. Some software platforms, such as QNX, have become popular in the SDV industry due to their advanced security features. In the future, artificial intelligence (AI) will play a key role in protecting SDVs from cyberthreats.

SDVs present a new business model for original equipment manufacturers (OEMs), one that centers around synchronizing software development with hardware. With the ability to deliver OTA updates to SDVs, OEMs and manufacturers must be able to validate new vehicle software and ensure regulatory compliance. A digital twin, which acts as a digital replica of the vehicle, can test different scenarios using data from different operational and geographical environments. By making changes to the digital twin model, OEMs can ensure those updates will work effectively without potentially compromising safety in real vehicles. In fact, companies are beginning to create fully virtual environments in which engineers can enter a simulated vehicle to test software changes before they go live.

With these changes in the automotive industry, OEMs must now develop vehicles that are compatible with multiple hardware suppliers and software providers across the supply chain, ensuring that any added applications remain compliant with safety and regulatory standards. For example, Volvo Group and Daimler Truck recently announced a partnership to co-develop an SDV platform for heavy-duty commercial vehicles. 

Given the complexity of SDV design compared to traditional vehicle architectures, simulation software can help to spot any problems early in the design and development process and rectify them before the physical prototype stage. For SDVs, simulation can aid in:

• Anticipating design problems and performing shift-left testing early in the process
• Analyzing the vehicle’s safety and cybersecurity functions
• Using AI to accelerate development
• Designing new electrical and electronic architectures
• Simulating at the chip and printed circuit board (PCB) level to ensure ECU components demonstrate thermal, signal, and power integrity
• Designing in-vehicle networks like sensors, antennas, and high-speed communications
• Simulating electromagnetic interference
• Analyzing system performance and reliability
• Developing high-fidelity, unbiased prototypes that integrate both software and hardware into designs through digital twins

Ansys offers many tools that simulate the range of engineering and design challenges that are presented within SDV development, primarily focusing on the hardware and integration aspects of the design. These include:

• The Ansys System Architecture Modeling (SAM) capability, which provides an authoritative source of truth as an architectural framework for capturing the description of the system being designed
• Ansys medini analyze software, a model-based systems solution that implements key safety analysis methods and ensures compliance with industry standards, including ISO 26262
• The Ansys SCADE platform, which enables model-based development, verification, and automatic code generation for safe and reliable embedded software
• Ansys ModelCenter software, which connects system requirements from the SAM with simulation models and engineering analysis to verify the behaviors and requirements of the entire system
• Ansys HFSS software, which ensures the efficient design and virtual validation of all electronics components, from chip to systems. HFSS software is also able to determine electromagnetic reliability to verify if electromagnetic interference will cause problems in the wider system.
• The Ansys Twin Builder platform, which creates fast-running reduced-order models (ROMs) of high-fidelity, complex models to accelerate solve times
• Ansys AVxcelerate Sensors software, which offers physically accurate sensor simulation for autonomous system testing with sensor perception
• Ansys AVxcelerate Headlamp software, which accelerates the development of smart headlamps by validating control law behavior and testing potential edge cases early in the design process
• Ansys SimAI software, an AI-enhanced tool that speeds up the design and optimization of simulations to accelerate component development

Contact our technical team today to learn more about how a simulation-based design approach can help you to optimize the design and manufacturing of your SDV components and interconnected systems.

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