Competition in the automotive industry is intensifying and brands are competing on more fronts than at any time in history. Of course, price, performance, brand, and residual values continue to be important. But as the industry gravitates toward electrification and software-defined vehicles, customers are looking at what else their vehicles can do for them. How well do they integrate with their lives and their digital ecosystems? Can and will the car evolve over time to add more value to daily life? And, for manufacturers, how do you build supply chain resilience and competitiveness to address these evolving demands, while ensuring availability and affordability? 

Automakers – especially at the luxury and premium end of the market – are also intensifying their focus on providing assisted and autonomous driving capabilities and new ways to add value with digital experiences, inside and outside the vehicle. In the face of increased competition, the speed with which automakers are able to innovate and the extent to which they can engage and satisfy their customers in new ways will be crucial to future success or failure.

Autonomous mobility at the crossroads

For years, tech and innovation events like CES have been dominated by autonomous vehicles of all shapes and sizes. The technology is always impressive … at the shows. But, in the real world, progress has been slower than expected. For every success, it seems like there’s been at least one story of a scaled-back or canceled investment, an unfulfilled promise, or a serious safety scare.

The pursuit of autonomous mobility is a double-edged sword. The cost of adding sensors for 20+ detection zones around the car is significant. And the volumes of data, the sophistication of algorithms, and the amount of computing power required to develop, test, and validate systems are eye-watering. And yet, the ability to offer customers safe and stress-free ways to travel; to give back quality time while getting from A to B, is a once-in-a-lifetime opportunity to build trust and open the door to a whole new world of services and revenue streams. It’s no wonder the pursuit of the various certification levels is so intense and why so many companies are taking different routes – from in-house development with tech partners to major alliances with tier-1 suppliers, and even acquisitions. Some companies are making more progress than others, but the race is still wide open.

The in-car experience is evolving

The transition to electric and the pursuit of autonomous-driving capabilities have major implications for the automotive customer experience, especially the in-car digital experience. With electric vehicles, we know that recharging away from home will involve idle time. And – though it may still be a way off – autonomous mobility will allow us to focus less on driving the car and leave us more time to do other things. Today, our first thought might be to reach for our smartphones or tablets, but this is a lost opportunity for vehicle manufacturers.

And so the question becomes: How can your car keep you entertained and engaged while it charges or self-drives?

The answers are emerging in the form of expansive screens, adaptive interfaces, the addition of extra screens for passengers, an increasing emphasis on in-car gaming, content consumption, subscription services, and almost unlimited ways to pass the time productively, recreationally, or relaxingly in a vehicle.

And then there’s the potential to have an AI-powered assistant, or companion, that connects all the different services and is capable of providing pretty much any information you need about your journey, your agenda, upcoming commitments, highlights from your inbox or social media feed, and much more.

All of these features represent potential points of differentiation, and many of them are revenue-generating opportunities (e.g. subscription-based services). Beyond direct revenue and new levels of customer intimacy, in-car digital interactions also create opportunities to generate new data and insights, which can (with the right levels of consent and anonymity, of course) be used to shape new products and services – inside and outside the vehicle – and new monetization opportunities.

Speed and satisfaction – why they matter more than ever

You could argue that the evolutions I’ve explored above are technology trends, much like many others. However, these trends are different in that if you can achieve the combination of safe autonomous or highly assisted mobility and engage customers with compelling in-car experiences, you can gain a level of trust, and access – and even companionship – that is unprecedented in the history of OEM-customer relationships. This brings with it the opportunity to develop deeper, longer, and more lucrative relationships.

But the race for the hearts and minds of customers is intense, with a raft of new players (many from China) to compete against, new demographics, and rapidly evolving customer expectations. In this climate of increased competition, it is imperative that automotive companies intensify their innovation efforts in a bid to deliver the integrated and connected customer experience that will soon be taken for granted. And if your brand isn’t able to provide it, you can assume that another one will. 

Balancing the need for speed and satisfaction with a ‘zero compromise’ approach to safety and security

Against this backdrop of ultra-intense competition and a relentless focus on innovation, OEMs must remain vigilant and understand that speed to market can never take priority over safety and security.

Assisted and autonomous mobility can offer comfortable, convenient, and stress-free travel. But they also mean taking a significant degree of responsibility for the safety of vehicle occupants. In short, ADAS and autonomous driving systems cannot fail. Failures will result in more than a few lost sales – they could lead to loss of life, high-profile court cases, and a complete loss of confidence in your brand.

And though it’s less likely to be a life-or-death matter, automotive brands need to be vigilant about ensuring the cybersecurity of their vehicles and data ecosystems. Digital assistance or companionship, subscriptions, services, integrated payment solutions and ecosystem services (e.g. via wearable health devices, smartphones, etc.) will typically require some degree of data sharing. This opens the door for personalization and seamlessly convenient experiences, but it’s not without its risks. No brand wants to be the next one to appear in a high-profile data leak story and risk losing the hard-earned trust of its customers.

Software is the key to safe, secure, and satisfying experiences

So what’s the key to accelerating innovation cycles and customer satisfaction without compromising on safety and data security?

The answer lies in your software strategy. After all, software is at the heart of assisted and autonomous driving systems, it drives immersive and engaging digital experiences through infotainment systems and more, and it can be the key to ensuring the security of personal data and the identification and elimination of sophisticated cybersecurity threats. The right software strategy and architecture (i.e. a simplified one) can also provide you with greater flexibility during times of supply chain instability, meaning you can maintain product availability while your competition potentially suffers. As many of us learned during the pandemic, simply making sure your cars are available to potential buyers can be the biggest advantage of all.

But the stakes are too high with software and the task of transforming into a software company is too big to go it alone. Here are three ways automotive companies can get their transformation right.

1. Partner up to boost software capabilities

Software-driven transformation is a broad and deep-reaching process, which can encompass upskilling your existing team, building new capabilities, and finding the right balance between maintaining your existing digital products and developing new ones. This is a huge undertaking, and so it makes sense to partner up with automotive software specialists and engineers who can share and instill industry best practices, build dedicated software factories for you, or support you in maintaining existing products or developing new ones.

2. Use cloud, virtualization, and AI to achieve more

Cloud and AI can be used to process and analyze the high volumes of data produced during autonomous driving system development and testing, to virtualize ECUs, and to support data spaces and service ecosystems. These technologies, combined with the suite of automotive-specific accelerators being built by hyperscalers today, can supercharge your innovation and product development cycles, enabling you to get to market faster with new products and services, while keeping your – and your customers’ – valuable data secure. 

3. Look for external inspiration

Automotive companies can’t be everything to everybody. It’s difficult (impossible?) to develop an infotainment UX that rivals that of smartphone makers like Apple and Google if it’s not your core business. Likewise, you won’t suddenly create ‘killer’ content and entertainment options if you’re just starting out. Instead, partner up with startups and niche players in differentiating domains and focus on the bigger picture.

The road ahead is filled with complexity and exciting developments. And yet, for all the focus on new technology, there are still large groups of customers who care little for new tech, and who continue to value practicality, build quality, and affordability above all else. How organizations address these oft-divergent customer desires within their product portfolio will be a challenge for many ‘traditional’ OEMs.

What we can say with confidence is that mobility experiences of the future – whether they’re autonomous or human-driven – must be satisfying, safe, and secure. Automotive companies must be quick to give their customers what they want. Check out our perspective on software in automotive to learn more. 

The semiconductor industry is standing at the edge of a profound transformation, thanks to the advent of a game-changing technology: chiplets.

Throughout its history, the semiconductor industry has pursued relentless integration and miniaturization. However, the escalating costs and complexities associated with cutting-edge Integrated Circuits (ICs) on advanced semiconductor technology have led to a revolutionary alternative approach: chiplets.

Most contemporary chips are designed as a monolithic SoC (System-on-Chip), integrating all essential functions—such as the processor cores, domain-specific hardware accelerator, memory, and interfaces — into a monolithic die, ie everything is built into an integrated circuit on a single piece of semiconductor.

Chiplets are a game-changer. They consist of a self-contained semiconductor die that, when combined with other dies through advanced packaging techniques, forms a complex integrated circuit similar to a monolithic integrated circuit. This modular approach enhances scalability, cost-efficiency, and performance. It also enables the integration of diverse functions, such as general-purpose processing, domain-specific processing, and memory into a single system, overcoming some limitations of traditional monolithic designs.

The chiplet approach not only addresses the challenges of rising costs and complexities but also unlocks unparalleled flexibility. Heterogeneous chiplet designs enable tailored solutions for specific applications or market segments. Solution providers can modify or add relevant chiplets without disrupting the overall system, resulting in reduced development costs and faster time-to-market, as redesign efforts only affect the package or additional domain-specific dies, not the entire chip.

There are still crucial challenges in the chiplet domain such as power and thermal management. Effective multi-vendor support is required to manage those aspects across all integrated chiplets seamlessly. And the standardization of interfaces and testing will be vital to ensure seamless integration, though, notably, organizations such as the Open Compute Project and UCIe (Universal Chiplet Interconnect Express) have already released specifications for open source chiplet interconnect characteristics.

Semiconductor giants such as Intel, Nvidia, and AMD have been quick to adopt chiplet technology, successfully demonstrating its viability in manufacturing, testing, and packaging as chiplet adoption gains momentum, the development of an ecosystem of suppliers is underway to serve its needs in areas such as packaging and thermal management. This will facilitate more widespread implementation across the industry, transcending adoption, reducing over-reliance on a few major players.

The growing popularity of chiplet designs has sparked interest across the entire semiconductor value chain, including Intellectual Property (IP) and Electronic Design Automation (EDA) vendors.

Beyond the leading semiconductor companies, the chiplet approach presents opportunities for design houses and semiconductor service providers like Capgemini. Collaboratively developed General-Purpose chiplet dies can cater to a range of vertical applications, for example serving a consortium of automotive companies pursuing in-car digital services. Additionally, Domain-Specific chiplets or custom dies can be tailored to meet specific requirements.

In conclusion, chiplets represent a flexible, adaptable, and cost-effective alternative to traditional monolithic designs. With its potential to revolutionize chip design, packaging, and integration, the chiplet paradigm is poised to redefine the semiconductor landscape, driving innovation and efficiency across the industry.

“I fight for perfection”

– Mike Tyson

“Do you achieve it?”

– Charlie Rose

“Nah! No one does, but we aim for it…”

– Mike Tyson

Put simply, deep learning is a type of machine learning that aims to mimic the way the human brain works to recognize patterns and make decisions. Deep learning on mobile and edge devices, like smart wearables with limited computational resources, can be challenging. However, running machine learning models on edge devices and keeping the data local has advantages for data privacy, sustainability and latency.

With one of our staff a keen boxer, we took on the challenge of working out what types of punches were being thrown with the constraints of:

• A need for real-time feedback
• Minimal hardware
• Low powered devices

How does it work?

Capgemini developed an end-to-end demonstrator, where data was collected using a custom iPhone and Apple Watch application, allowing sensor readings to be automatically labeled. A hybrid deep learning model was built to accurately classify each punch type. The model was converted to an optimal form to run efficiently on low powered edge devices.

The demo streams raw sensor data from the watch to display on a screen, along with the punch classifications, as close to real time as the network connectivity allows. In this case, we’re using a single Apple Watch on the left wrist to generate sensor data (specifically accelerometer, gyroscope and orientation data). This data is sent to an AI model that determines which types of punches are being thrown by either hand.

When we demonstrate this to the attendees at various events, the reaction is often “Wait – how is that even possible?!”. Indeed, how do you know what the right hand is doing with a watch strapped to the left wrist? While it’s certainly easier to track the lefts, boxing is a whole-body sport – there are small but characteristic movements that happen in response to throwing a punch. These movements are what the ML model detects, and uses to classify the type of punch.

In a world where seemingly everything is smart and sensors are everywhere, this may seem like an artificial constraint, but often you can’t put sensors right where you want them – for example, due a harsh industrial environment or because they would be too cumbersome or intrusive for the wearer.

To run on low powered edge devices and give near real time results, the machine learning model had to be as lightweight and efficient as possible. Through a careful selection of model architecture and the type of optimization techniques discussed in this blog post on model optimization, we produced an AI model that could interface in real time on a low end smartphone. 

Applications

Our technology has potential applications across many domains. For example, you could extend this application to give feedback to a novice boxer and help them avoid common mistakes – the minimal hardware would make this a very portable solution. For patient monitoring, perhaps when managing chronic illness, ensuring privacy is crucial and minimizing the number of sensors could help ensure compliance and guarantee that solutions are minimally invasive and cost effective.

In industrial settings, being able to classify in real time close to where data is being generated allows for rapid intervention. It can also help to reduce the cost and associated carbon emissions from the transfer of large volumes of data.

In disconnected applications, like drone operation in remote locations, this approach can be used to improve autonomy, for example allowing these drones to locate a safe place to land in an emergency, through on-board real-time video analysis.

Conclusion

Capgemini is exploring the applications of this technology in sports equipment, but we believe that this is just the beginning; this technology certainly isn’t limited to the complex movements of the ‘sweet science’. Whether based on incoming sensor, video or other data, being able to analyze inputs in real time on low powered devices has a crucial part to play in unleashing the potential of intelligent products and services.

Want to see in action? Watch our boxing demo video

Interested in finding out more? Take a look at our Intelligent Products and Services offer and follow David on LinkedIn.

Why has the European Commission prioritized data?

With the publication of the European Strategy for data in 2020, the European Commission set out to “become a leading role model for a society empowered by data to make better decisions – in business and the public sector.” The strategy rightfully described data as “an essential resource for economic growth, competitiveness, innovation, job creation and societal progress in general”. The expected value-add of data is significant: the European Commission estimated that the value of the data economy in the EU27 would reach €829 billion in 2025.

From open data to data spaces

Often the notion of data sharing is first and foremost associated with open data, i.e. data that anyone can access, use and share free of charge.

Notably, the European Commission saw the value of open government information and data with the Public Sector Information (PSI) Directive at least 20 years ago, which was subsequently replaced by the Directive on open data and the re-use of public sector information in 2019. In retrospect, both directives were indeed pioneering efforts.

However, despite these efforts, only a tiny fraction of extant government and private sector data has been declared as open data – and hence is being shared. In order to broaden the scope of data sharing in the European Union and to “bring benefits for citizens, for example through improved personalized medicine, new mobility and through its contribution to the European Green Deal” the European Strategy for data formulates further actions around four pillars: first, a cross-sectoral governance framework for data access and use; second, investments in data and strengthening Europe’s capabilities and infrastructures for hosting, processing and using data and enhancing interoperability; third, empowering individuals, investing in skills and in SMEs and fourth, common European data spaces in strategic sectors and domains of public interest.

What is a data space?

“Traditional” data sharing is hampered by various organizational, technical, budgetary, and legal barriers – which organizations that want to share data largely need to overcome by themselves. Perhaps even more importantly, public and private sector organizations often lack incentives to incur the costs for data sharing and fear that sharing data with third parties will expose them to governance and business risks.

The European Commission’s concept of data spaces is aimed at overcoming these barriers. Specifically, the European vision of data spaces includes data-sharing tools and services, a decentralized approach, fair and transparent governance provisions for data access and provisioning rights, as well as standards to improve the availability, quality and interoperability of data.      

Hence, and somewhat counterintuitively, a data space should not be thought of as a forum or platform, but more as a mode of data sharing where organizational and individual actors pool and exchange data in a way that is safe, fair, practical and clear.

Data spaces in practice

The 2020 European data strategy announced the creation of ten data spaces in ten sectors and domains “where the use of data will have systemic impact on the entire ecosystem, but also on citizens”: health, agriculture, manufacturing, energy, mobility, financial, public administration, skills, the European Open Science Cloud and the overarching priority of meeting the Green Deal objectives. The identification of specific sectors and domains is well-founded as it allows the respective data spaces to cater to differences in business use cases, data sharing incentives, degree of public interest, and regulatory standards.

Since 2020, the EU has issued additional regulations, directives or legislative proposals to boost data sharing, such as the Data Governance Act or the Proposal for a Regulation on the European Health Data Space.

Also, the necessary funding has been released and the work on setting up the data spaces has commenced. Most of the data spaces mentioned above are still at a formative stage and are currently focusing on delivering project outputs and deployment roadmaps. Some, however, have progressed further and are in the implementation or the operational stages.

Thanks to its ability to draw on the prior experiences, infrastructure and the solid data governance and model of the cultural heritage project Europeana, the Data Space for Cultural Heritage (DS4CH) has advanced particularly well. Not only does it already provide access to digital cultural heritage and digital learning resources and allow participants to exchange data, it also serves as a role model by providing technology products and services in an open-source approach for reuse.

The road ahead

Data spaces hold great potential for the way that public and private data is used and shared – and can help drive social and economic innovation in Europe. Hence, we strongly believe that it is worth paying closer attention to the development of data spaces, and that both public sector and private organizations should examine the opportunities data spaces hold for them more seriously.  

Granted, the European Commission’s data spaces endeavor is ambitious, complex and still needs to come to full fruition. On the upside, the extant challenges and necessary steps to overcome are becoming increasingly well-understood. For instance, in November 2023, the EU’s Data Sharing Support Centre (DSSC) – which we support along with our consortium partners –  issued recommendations for the future roadmaps for data spaces. These aim to boost the technical and infrastructure foundation for fully operationalizing data spaces, enhancing interoperability within and across data spaces, and facilitating collaboration with other data spaces.

The DSSC also offers a starter kit  that provides guidance for individuals and organizations that want to establish or take part in a data space. Furthermore, there are various organizations and associations such as Gaia-X, the International Data Spaces Association (IDSA) and Mydata Global that are committed supporters of the principle of data sharing and offer their deep expertise and wide-ranging support as well.

If you are interested in learning more about data spaces and how to reap the data sharing dividend, please have a look at our Connecting the dots research and our view on collaborative data ecosystems. We are of course also more than happy to discuss your views and questions on data sharing and data spaces.

The car of the future will be very different to the one of today. As vehicles become digitized, high-end cars may change from a luxury driving experience to a luxury chauffeur experience, with music, mood lighting, or wellbeing features. A car interior could adjust its layout and color at the touch of a digital dashboard, to become a conference room, a meditation temple, or a bedroom. Other uses will arise that we can’t yet imagine.

But today’s market is increasingly competitive – with a long-term downward trend in vehicle ownership. Premium carmakers must stay competitive, and the in-vehicle user experience will be a key element of this brand differentiation. Successfully delivering this new vehicle user experience requires significant changes, both to the vehicle and the ecosystems around it, as well as the skills, culture and business models of carmakers.

In the first part of this two blog series, we will discuss the key ingredients of this new Mobility Experience.

What will success take?

It will ultimately come down to three areas:

1. The in-vehicle user experience
2. The vehicle communications system
3. The mobility services ecosystem

These are outlined below.

Requirement 1: In-Vehicle User Experience

The in-vehicle experience depends on elements of the vehicle that enable new and richer experiences for the driver and passengers. Such elements include head up displays and windscreen AR overlays which can present driver insights.

It includes infotainment, which naturally includes screens and speakers, but may also include smart lighting, connected temperature and movement sensors and seat adjustments (eg. actuators and heating pads). The future mobility experience will also include functionality we haven’t even thought of yet – this is really just the beginning.

Getting this right requires a range of hardware and software that must be designed, deployed and regularly updated, in order for new digital experiences to be easily (and securely) uploaded to the car’s computer.

Requirement 2: Vehicle communications

The key driver of the digital user experience is connectivity. This allows vehicles to add new digital services, make updates, and share data to generate real-time insights, as well as talk to smart homes, and smart city infrastructure.

Good connectivity means implementing Wi-Fi, cellular, Bluetooth and other wireless communication technologies which in the future will likely be extended to include Low Earth Orbit satellite communications. It will also mean integrating the communications protocols and software that allow these to operate reliably, and communicate via the car’s telematics control unit (TCU). Cellular is probably the most pressing challenge.  Most vehicles can already connect over Wi-Fi, but that relies on a local signal. Cellular – especially using new 5G networks – would allow ‘always on’ connectivity that allows real time data processing, over-the-air updates and features on demand.

Requirement 3: Mobility services

This last bit is about launching software services, or apps, which interact with the in-vehicle technologies to offer new user experiences, such as restaurant recommendations, or information about points of interest the vehicle is passing.

Work must be done to design the in-vehicle systems to accommodate such apps, and set up processes to safely download and integrate them. Vehicle makers may have teams working on their own apps, but even these will need processes for certification, and dedicated channels in app stores. This will be doubly important if OEMs wish to welcome third-party applications.

Want to find out more?

In the second part of this blog, you’ll learn what it will take to redesign vehicles for smart mobility: Part 2: How to set yourself up to redesign the car around the user.