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Written by: Nand Kochhar, VP of Automotive and Transportation Industry Strategy, Siemens Digital Industries Software

The automotive market is undergoing a transformation. New technologies, consumer demands, environmental pressures and more are driving a race to reinvent the personal driving experience by making it more comfortable, convenient, safe and sustainable. As the appetite for high-tech vehicles continues to increase, and particularly as autonomous vehicles (AVs) come closer to becoming a reality, the nature of vehicle development has changed. These advanced vehicles are defined by the software and electronic components that enable the advanced features desired by modern consumers. This transition towards a software-defined vehicle has caused a rapid growth in vehicle complexity as they become increasingly smart and connected.

AV Systems Drive Complexity

AVs will require a constellation of advanced technologies and engineering domains to achieve true self-driving capabilities. It is this confluence of technology and engineering disciplines that creates such immense complexity. This includes mechanical, electrical and electronic systems as well as embedded software, artificial intelligence, machine learning and communications networks all working together to deliver self-driving functionality. In an AV, these domains intersect to a greater degree than they have previously, ensuring that no system within the vehicle is truly independent.

Advanced driver assistance system (ADAS) features available today offer a preview of the cross-domain complexity we can expect in a true AV. A lane-keep assist system, for example, requires sensors, processors and software to perceive road lines and calculate vehicle trajectory before activating mechatronic components in the steering system to exert control over the vehicle. Yet, even the most advanced ADAS systems available today only achieve level-3 self-driving capabilities.

The jump to level 4 represents a major shift in the progress towards AVs. At level 4, control over the vehicle shifts away from the human and rests entirely with the machine—though only within specified operational conditions. While a level 3 system may require the human driver to take control under certain circumstances, a level 4 system must always exert total control over the vehicle when its operational conditions are met.

Further progress on the road to full autonomy will depend on the maturation of several key technologies. Vision sensors, such as cameras and LiDAR arrays, must be able to accurately identify other vehicles, pedestrians and myriad other objects in a variety of weather and lighting conditions. Ensuring these sensors can operate reliably despite adverse conditions is a critical step towards level-4 vehicles.

Next, onboard computing power will need to increase significantly to process the immense amount of incoming sensor data at near real-time speeds. In addition to improving raw computing power, the continued development of AI and ML can help vehicles process data more efficiently and quickly.

Finally, vehicle-to-vehicle, vehicle-to-infrastructure and vehicle-to-cloud communications will need to become more widespread to enable AVs to operate as a system—rather than as individual vehicles.

The inherent challenge is that as these technologies mature and move the industry closer to level 4 and level 5 AVs, the complexity of the vehicle grows in all domains: hardware, software, mechanical, electrical and networking. A level 5 AV replaces all the “sense-think-act” functions a human driver may need to perform with software, electronics and mechanical or mechatronic components. Cameras, LiDAR, RADAR and other types of sensors act as the eyes and ears for the vehicle, perceiving the driving environment and providing information to the other systems within the vehicle.

Computer chips (specifically system-on-chip devices) and advanced software combine to act as the “brain,” taking in sensory information for processing and decision making. High speed network connections can be thought of as “nerves” that carry information from the sensor systems to the processing core and transmit instructions in the opposite direction. And finally, the mechanical and mechatronic systems that act as the “muscles” as they convert electrical signals into physical motion to provide steering, braking, acceleration and more.

The result is a vehicle that is more and more of a “computer on wheels,” containing a vast number of advanced components networked into one system. This includes bleeding-edge SoCs, sensors, electrical networks and mechanical systems, as well as a quickly ballooning amount of software embedded in each vehicle.

Building Trust in the Market

In addition to growing product complexity, AV manufacturers are also dealing with a consumer base that is unfamiliar, and sometimes wary of such a new technology. Most consumers have only limited exposure, at most, to automated driving technologies. If the public lacks trust in the technology, then adoption would likely stall. How do AV manufacturers prove to consumers that their systems are safe and reliable even in adverse conditions? Pilot programs and test drive opportunities may help relieve the concerns of a portion of the public, but such direct exposure at a large scale is impractical. Instead, manufacturers can participate in the creation of testing, verification and validation standards for AVs as a means of demonstrating the capabilities of their systems.

Standards and regulations allow companies and governmental organizations to cooperate on the definition of safety ratings, minimum reliability requirements, operational conditions, testing and verification programs that can sufficiently prove the safety of an automated driving system. By establishing benchmarks for vehicle safety, reliability and other characteristics, companies can demonstrate the competence of their self-driving systems to the public, without relying solely on test rides or other in-person events.

As standards for AV safety and reliability are established, automakers will need to develop testing, verification and validation programs to show that their AV systems can meet these benchmarks. These programs will need to be extensive.

Rand Corporation has calculated that it requires more than 17 billion kilometers of test driving an AV to demonstrate a failure rate significantly better than humans. This volume of testing is necessary to investigate the non-routine scenarios that are difficult to predict and thus account for during development.

Clearly, accomplishing all this testing with real-world prototypes is not feasible. Instead, real-world testing must be augmented with high-fidelity simulations that can help design teams gather vital information more quickly and cost-effectively. With a combined approach, AV engineering teams will be able to investigate and account for exceptional on-road scenarios more effectively, thus improving the safety of their AV systems.

The Power of Digitalization and Systems Engineering

Companies hoping to participate in the self-driving vehicle revolution, including legacy automotive original equipment manufacturers (OEMs), suppliers and new market entrants must be able to deliver innovative software-driven vehicle features and contend with the mounting complexity of integrating these features into the mechanical, electrical and electronic systems of a vehicle.

To do so, automakers must rethink traditional vehicle development processes and embrace digitalization to compete in this dynamic and crowded marketplace. Critically, automakers must capture the vehicle holistically, as a system-of-systems, including hardware, software, networks, electrical wiring and mechanical systems to understand the complex interdependencies within a modern vehicle. With such a perspective, automakers can drive innovation at the rapid pace demanded by the market today. Companies that implement this approach with coherent model-based dataflows will enable their engineers to design and refine functional models of various vehicle systems, optimize details across the product lifecycle and connect these models together.

It is critical for automakers to embrace this change and digitalize the entire vehicle design, development, validation, manufacturing and utilization lifecycle. Digitalization can help to address the immediate challenges automotive companies face today. But it also provides the foundation for growth and success tomorrow. The automotive companies that embrace the advances in digital engineering, simulation and lifecycle management solutions will be better prepared to overcome the complexity of autonomous vehicle development.

At the heart of this transformation is the concept of a comprehensive digital twin of the vehicle, covering every aspect of the vehicle and its associated production processes over its entire lifecycle. The digital twin is a way to address the challenges of complex vehicle system development by building a set of highly accurate models that help predict product behavior during all lifecycle phases of the vehicle.

The comprehensive digital twin is the ultimate version of this concept, supporting model-based design of the product and production process, integrated manufacturing operations management and a cloud-based data analytics feedback loop from the product-in-use back into the digital twin. Such a digital twin becomes the backbone of product development—capable of delivering greater insight, reducing development cycle time, improving efficiency and increasing market agility.

The digital twin will act as the foundation for the collaborative, integrated approach demanded by modern vehicles, bringing the mechanical, electrical, electronic and software domains together to design a complete system. The comprehensive digital twin can also support the immense verification and validation requirements of a self-driving system.

High-fidelity simulations of the comprehensive vehicle digital twin enable engineering teams to use the knowledge from known real-world situations in combination with proven mathematical methods to uncover safety-critical scenarios. Doing so in a virtual environment allows teams to uncover and analyze these scenarios much more efficiently, reducing the number of unknown safety-critical systems.

Next, modern simulation solutions can support full vehicle verification and validation in various environmental and traffic conditions, right down to the individual sensors, electronic control units, software and computational units. These solutions allow companies to test various systems and even entire vehicles in virtual environments before committing to the expense of prototyping and physical certification. This makes early virtual verification and validation through high-fidelity simulations ideal for newly identified “corner cases.” When paired with digital design and engineering environments, learnings from these vehicle simulations can be quickly integrated back into the system designs.

The Digital Future of the Automotive Industry

Digitalization will empower automotive companies as they pursue higher levels of vehicle autonomy. These enhanced vehicle features and systems will rely on an ever more complex underlying architecture of powerful computer chips, sensors, software, electromechanical subsystems and electrical wiring. This complexity will be felt across the vehicle development lifecycle, from design to verification, testing and production. Widespread digitalization will also support new approaches to vehicle development that encourage cross-domain collaboration and support data coherency throughout the product lifecycle and the ability to capture and analyze data from testing and the field.

Finally, it is important to recognize the value of strong technology partnerships to the success of digital transformation initiatives. Companies like Siemens have been working with customers from all industries to develop digitalization plans and to build powerful digital twins of products, production processes and more. A technology partner can offer a robust portfolio of digital capabilities, consulting and engineering services that can reduce risk in a digital transformation program and accelerating progress to key milestones. Finally, they can also deploy technologies in the manner that best fits the company’s needs: on-premises, in the cloud or through a hybrid cloud approach. With the right support in engineering technology and services, automotive companies can overcome the challenges of AV complexity and the digital future of their industry.

Learn more at Siemens Digital Industries Software.