Agile Development in Automotive [Pt. 3]

The automotive industry has traditionally been a bastion of structured development. Yet, as the digital age advances, there’s a growing need for more flexible practices. Unlike more traditional development processes, the agile methodology is non-linear and allows for increased adaptability — especially when making last-minute changes.

This is the final article in our three-part series (part 1| part 2) where we explore how AI impacts the automotive industry. In the first, we dove into the need for new tools in the software development process while the second looked at the need for innovation in process as well as technology.

Embracing controlled agility 

Agile development in automotive isn't about recklessly pushing new versions — though it might work that way in other industries. Cars are safety-critical machines, and there's no room for error. But this doesn't mean the industry can't benefit from an agile approach

By implementing controlled agile processes, developers can ensure small updates don't adversely impact the entire vehicle. This approach allows developers to make small, iterative changes while still meeting automotive industry regulations.

Benefits of iterative development 

From a developer's standpoint, the agile approach offers many advantages. Humans find it challenging to tackle large tasks head-on so it’s practical to break things down into more manageable pieces. When presented with a large task, like developing a brand new function, it can feel overwhelming. Breaking this down into smaller, more manageable chunks better fits natural human behavior, allowing for more productivity and flexibility. 

Developers can be more efficient by focusing on specific functions in phases and releasing them in small steps. This iterative process not only increases productivity but also ensures that each function is thoroughly tested before proceeding. This is vital when it comes to vehicle safety.

How AI tools can support the agile development process

Artificial intelligence tools have the potential to streamline the development process. For instance, consider the task of tracking bugs in a system. Traditional systems often require manual searches, leaving developers feeling like they’re looking for a needle in a haystack. 

AI can assist in understanding customer needs, generating tests, and ensuring that these tests align with requirements. This technology can help developers resolve errors more quickly by using AI to map the entire software system and give insights into exactly which lines of code have changed, which need testing, and which are causing issues. This helps to track down bugs, including those from unpredicted scenarios and edge cases that might otherwise be difficult to find.

Automating these aspects allows developers to be more agile in their software development and testing by getting faster quality feedback and enabling them to focus on what they do best: developing innovative solutions for the automotive industry.

The automotive industry is on the cusp of a significant transformation. As software-defined vehicles become the norm, the need for agile development becomes more important than ever. By integrating these methods and leveraging AI tools, developers can better innovate while improving efficiency.

 

Click here for more insights into the future of automotive software development.


Part 1| Part 2

Balancing Innovation and Process in Automotive Software Development [Pt. 2]

As the automotive industry grapples with the challenges of modernization, it's essential to understand that innovation in automotive software isn't just about the development of new features. It's equally about refining and redefining the processes that support it.

This is the second article in our three-part series where we explore how AI impacts the automotive industry. In the first, we dove into the need for new tools in the software development process.

The need for process innovation

Software innovation is undeniably crucial, especially as consumers begin to demand more high-tech features such as autonomous driving capabilities. However, the real challenge lies in ensuring that developers are also innovating the processes used to build automotive software. Changing human behavior, especially in a legacy industry such as this, is no small feat. The serial production of software — which is akin to a manufacturing production line — may offer control but is no longer the most efficient or effective way to approach software development.

What’s needed is a more agile approach (more on this in the third part of this series), one that involves smaller steps and innovative testing methods, such as virtual environments. Complex software demands process innovation, and the industry must rise to meet this challenge.

As we mentioned in our previous article in the series, the traditional way of matching customer requirements with the finished product was through the V-shape model. This is the way things have been done for a long time but it’s time-consuming and, often, inaccurate. Innovation isn’t just about adopting new technologies, it’s about thinking outside of tradition and considering what new processes might be possible with advanced tools such as those using AI.

The cultural shift

The journey to process innovation is as much about culture as methodology. Developers, who are often bogged down by the daily grind of fixing bugs and releasing software, may overlook the need to reevaluate their processes. As more tech-forward companies bring agility and innovation to the table, however, larger OEMs are beginning to take notice. These industry giants are now seeking insights from agile startups, indicating a promising shift toward a more collaborative and innovative future.

For developers who are already thinking in this way, it’s a case of looking at requirements, development, and testing, then considering how those processes could be improved using technology or new ways of working. 

A good way to start thinking about this is in terms of the challenges. Within the traditional methods of development, what isn’t working? Perhaps the testing process takes too long or it’s difficult to get updates out on time. Maybe it’s external problems such as supplier deliveries that are causing issues. Whatever it might be, an innovative approach to the process could be the answer, especially when backed up by technology.

For example, if the testing process feels cumbersome, the question should be asked, is running all tests, no matter the software changes, the best way of achieving software quality? Switching to a process that incorporates AI to detect the changes in the software and analyze the potential quality risks could be the solution. For example, Auto Detect from Aurora Labs can efficiently select which tests have the highest probability of failure due to the changes made in the software in the specific build. Rather than running all tests available, this means only the necessary ones will run, significantly reducing time while still ensuring test effectiveness.

Shifting left

The cost, both in terms of money and resources, of detecting and fixing software problems increases as the software progresses through the software lifecycle. For example,  traditional methods of software updates come with certain limitations, especially when it comes to the speed at which manufacturers can release updates. For instance, updating car software traditionally involves inefficient processes that are integrated and implemented after the software has been developed and installed in the vehicle ECU – a cumbersome and data-intensive process that’s far from cost-effective. However, AI technology, such as Aurora Labs’ Auto Update, now allows for software updates to be integrated as an integral part of the software development process and not as an afterthought. 

To leverage the power of this technology, it’s important to build the tools into the Continuous Integration and Continuous Deployment (CI/CD) process. This not only makes the process more efficient but also paves the way for faster and more agile software development.

As the automotive industry continues its journey into the digital age, the balance between innovation and process will remain at its heart. By embracing agile methodologies, creating a culture of continuous improvement, shifting left with new technologies, and integrating AI tools, legacy automakers can ensure they keep ahead of the curve in a rapidly evolving industry. 

If you’d like to learn how artificial intelligence could bring innovation to your software development processes, get in touch here.

Part 1| Part 3

Continuously Better — a Recipe for Winners

Making devices, processes, and even people continuously better is not a new idea. Case in point -- we were watching a documentary about Henry Ford last week and one of his main business principles was 'good isn't good enough', and he continuously made improvements to an already revolutionary factory floor. He implemented processes and technology -- mainly the conveyer belt - to make sure building the Model T was more seamless, bringing automotive parts to employees instead of employees going to find parts. Fast forward almost 100 years, and the same principle is executed by successful companies.

The browser wars were won because Google ensured Chrome was always being improved with seamless updates increasing speed, enhancing security and introducing new features. In the automotive world, where software is now a driving force, vehicle manufacturers are continuously making the consumer experience better with over-the-air software updates. The problem is they are not always seamless.

For example, a friend recently received a letter from his car manufacturer explaining that an update to fix the infotainment system was available. The options were to take the car to the dealer or to update the vehicle on his own. A link to instructions on how to do the update himself was in the text of the letter. Going to the noted website, he landed on a YouTube page with a video of how to do the update. When he went to his vehicle to follow the instructions -- they were totally incorrect, and the UI in the video didn't even match the UI in the vehicle. Not very seamless.

Tesla is always used as an example of how to best do seamless over-the-air updates offering new features and functions that consumers will look forward to and enjoy. On the other end of the spectrum, there are those that think updates are "Big Brotherish" and should not be allowed at all.

However, continuously better will always win. But, unlike mobile phones and laptops, 'continuously better' for the vehicle requires a great deal more effort on technology testing, quality assurance, third-party certification and regulation. The generation coming up in the world expects their vehicle to mimic their phone, and they want the same user experience. The generation building these solutions today is responsible to make sure 'continuously better' keeps the next generation safe while simultaneously meeting their expectations of personalized and satisfying user experiences.

Success comes from innovation

With technology and automation developing at an extraordinary rate, accelerated even further by the effects of the pandemic, traditional automotive manufacturers are working hard to bring innovation into everything they do. The most prevalent trend in 2022 is autonomous vehicles (AV), closely followed by connectivity and electrification. Perhaps unsurprisingly, nine out of the top 10 automotive innovation trends are technological.

Automotive OEMs (original equipment manufacturers) are in a prime position to instill a culture of innovation more deeply within their businesses. It's something that's so pervasive within the industry that they can and should be taking inspiration from that atmosphere, adopting new technologies and ways of working, and bringing in new people in order to keep things fresh.

Innovation in automotive right now

According to Forbes, the automotive world is set to completely transform in the next two decades, much as it did in the late 19th century. At this time, the world's major cities were awash with manure as the use of horse-drawn vehicles reached its peak, but by 1912 Henry Ford had resolved to solve this issue with the motor car.

Another dramatic shift of this type seems feasible when you consider the speed and breadth of innovation in this industry. Electric vehicles (EVs) are now commonplace, and Deloitte's global EV forecast shows a compound annual growth rate of 29% over the next decade. Additionally, EV sales growth is expected to expand from 2.5 million in 2020 to 11.2 million in 2025, and 31.1 million by 2030.

Gartner has reported that the automotive electronics sector will experience the biggest semiconductor compound growth rate up to 2024, at 9.3%. Modern vehicles have around 8,000 semiconductor chips and over 100 electronic control units; these currently carry over 35% of the total vehicle cost, but that is expected to rise to 50% by 2030.

Software-defined vehicles

Then there are the vehicle manufacturers that have had technology and innovation at their core from the beginning. Software-defined vehicle companies like Tesla are taking a different approach and are constantly thinking outside the box. Its EVs are some of the fastest in the world, it's working on transformative energy projects to help limit fossil fuel demand, and it even considers elements like how professional drivers will be affected by autonomous self-driving. That in itself is innovative.

While more traditional OEMs are embracing a similar shift, their approach is fundamentally different. The automotive industry has been at the forefront of technological innovation since it started but with the rise in software-defined vehicles, there's a need for more innovative out-of-the-box thinking than ever. The key is to build a culture of innovation within the a company, whether it's been around for 100 years or just 10. This means embracing new technology, leaning on artificial intelligence tools, and looking for innovative ways to stand out in a market that's more competitive than ever.

Challenges

According to Forbes, some of the newer 21st-century vehicle manufacturers are challenging -- or even overtaking -- the major established players. Ford ($53bn), BMW ($62bn), and GM ($82bn) have Tesla ($64bn), Nio ($70bn), and BYD ($65bn) nipping at their heels, meaning traditional OEMs have to move quickly as the automotive industry continues to evolve.

The biggest challenge lies in having to become, essentially, a software company. Many OEMs have traditional mechanical production company DNA, and turning that into something much more software-focused is a difficult thing to pivot to. Despite this, companies such as BMW, Porsche, Hyundai, and many others have been able to embrace a culture that doesn't just look at ways to innovate when it comes to their products but also in their manufacturing methods, over-the-air-updates, their sales processes and more.

Investment in AI tools -- such as Vehicle Software Intelligence -- infrastructure, and employee training will all be key for traditional manufacturers when it comes to building a culture of innovation.

How to build a culture of innovation

We've established that the main difference between traditional OEMs and the more modern ones is that the latter started as software companies, and the former is taking steps to pivot their approach. Overcoming this challenge starts with getting the right people on your team. This is what creates the new kind of thinking that's required to keep up with modern automotive demands. Other ways to build a culture of innovation include:

  • Employee training and workshops that focus on new ideas
  • The adoption of cutting-edge tools that improve processes
  • Embrace AI and other technologies that can increase automation and free up employees for tasks that need a human touch
  • Adopt agile processes to speed up the delivery of software and updates

It's also important to know when to innovate. Consumers don't necessarily want a vehicle that looks and drives like it's straight from science fiction, but perhaps technology can improve the driving experience in more subtle ways -- such as in advanced safety features or vehicle upgrades delivered over the air.

Additionally, if AV is the biggest trend in automotive right now, then AI is an ideal area to focus on. In fact, AI and machine learning are important across the entire value chain -- not just when it comes to driverless vehicles. These technologies can improve time to market, development processes, and quality control. Software-led innovation is an opportunity that allows OEMs to maintain competitive advantage and growth. To learn more about Vehicle Software Intelligence and how it can solve your own challenges, contact Aurora Labs today.

AI insights increase software quality in the automotive industry

While many people associate artificial intelligence (AI) in the automotive industry with autonomous vehicles, it's actually a powerful tool that's driving software development too. I recently joined James Carter and David Fidalgo on the Byte Off Podcast to talk about the impact AI is having across the industry.

AI has the potential to improve the outcomes for quality and support engineers during the automotive software development process. Here at Aurora Labs, we're using AI to recognize patterns in the behavior of the software, as how it behaves indicates how it runs. By identifying these patterns, you can begin to predict when and how a piece of software might fail before it actually does.

Using the right AI tools (such as Vehicle Software Intelligence) we're able to help car manufacturers find problems in their vehicles before they cause failures. This allows them to focus on improving quality instead of running around trying to fix problems.

The challenge in using AI tools across these areas is correctly identifying when it's appropriate. A lot of people see this technology as a silver bullet that will fix all sorts of problems, but it's actually most powerful in areas where the inputs are unknown or the variables are great.

This is why it's so often associated with autonomous driving because the technology has to be smart enough to understand that every road, every car, and every tree looks different and still be able to identify them as such. The technology needs to be able to recognize these patterns and learn from the information it is fed.

The shift left

What we're seeing now is a shift left, which means we're starting to use AI much earlier in the development process. The idea is to catch problems earlier as this makes them easier to fix, keeps costs down, and saves valuable time. It's similar to the process of building a house. If you find a problem in the construction of the walls and identify this early on, it's much cheaper to fix the issue than if the issue had been discovered after the house was complete.

The shift left in the automotive development world is similar. It's about moving your quality tools and insights earlier in the process so you're not leaving everything until the end. Fixing issues early on is much less expensive than patching them with over-the-air updates or worse, recalling your vehicles.

There are other trends influencing this shift. Both the move to CI/CD and agile software development methodology play a role. This means a car that might have been designed over six years, for example, can now be designed in a much shorter period. With these shorter development cycles, it's vital manufacturers are testing their software early enough in the process so as not to cause delays further down the line.

Another trend is the move toward the software-defined vehicle. With the software disconnected from the hardware platform and any specific model year, there needs to be more focus on the quality of that technology as it's driving so much within a vehicle - even across different models and generations, in some cases. With this, CI/CD, and agile workflows, there's an openness to try new AI tools to improve quality and give actionable insights early on at a much lower cost than you might have with more traditional development methods.

Testing the modern vehicle

Because of the complexity of a modern vehicle and now, the option to add features via a subscription, existing testing methods become far more difficult. If you're trying to write test scenarios for every permutation of variation and in every configuration, you can very quickly get to a point where an engineer physically can't write all these tests - and you certainly don't have enough time to run them, even with automation tools.

AI algorithms, however, can monitor the behavior of the software as it's being run and pick up on deviations automatically. Without any manually defined thresholds, the AI is able to detect changes in behavior. This allows engineers to focus their attention on what is changing and what could potentially affect the vehicle quality and performance.

This benefits both end-users and OEMs. The customer gets their update or subscription feature immediately and can trust that the new software isn't going to affect something else in the vehicle. Manufacturers, on the other hand, are able to improve quality quickly and more affordably while keeping customer satisfaction high.

Artificial intelligence is a powerful tool and something the industry is becoming increasingly open to. If you'd like to find out more about automotive software quality assurance take a look here.

Part 2 – Challenges of Using Software as a Revenue Generation Tool

While both new car manufacturers and traditional OEMs are embracing software updates as revenue generation tool, there could still be some bumps in the road ahead. There's no doubt that adding additional features and upgrades to a car after the initial sale can drive new revenue streams (see part one of this blog series) for carmakers, but all great opportunities come with their share of challenges.

Currently, the cost and complexity involved in overhauling legacy plated processes mean some OEMs have forms been slow to adopt OTA updates as a method of delivering feature and firmware upgrades. However, legislation, new regulations, and maintaining the user experience could present an equally sizeable barrier.

Cost

As vehicles become more sophisticated, the cost to keep them up-to-date with the latest features could easily spiral out of control. Some methods of updating a vehicle require huge amounts of data to be stored and transmitted for every update. Both full-image and binary updates could see costs run into the millions for cloud storage alone. To provide the type of updates demanded by consumers, OEMs need to look for ways to reduce these costs.

One method is through Line-of-Code Intelligence, which doesn't fully overwrite the flash storage in a vehicle. Instead, it just updates what is necessary and writes to the next available space on the chip. This can help to reduce costs (as there's less data to store and transmit), as well as improve the experience for the end-user.

User experience

As a driver, there's nothing more frustrating than jumping in your car only to find out you have to wait for the vehicle to update before you can use its core functionalities. If manufacturers are to deliver new features to a vehicle in order to increase revenue, the experience needs to be seamless.

Because, full-image and binary updates erase the previous code, the driver would need to wait while the car is being updated. In most cases, this shouldn't take too long, but it's far from convenient. Line-of-code updates are a little different, however, and allow the driver to continue on as normal with no break in how they use their vehicle. This is because the previous code isn't erased, so the old version of the software can continue to run while the update is being delivered.

Safety concerns

While many manufacturers are currently able to make updates as needed to their vehicles, some experts have safety concerns, arguing that novelty and performance features could cause problems. Even where OTA updates are delivered to improve safety, the argument is that these may not have been adequately tested in the same way they would be at the point of manufacture.

Legislation is coming into place that references OTA updates, how they're tested, and the impact of new safety features. This is on top of insurance validity concerns around changing the functionalities of a vehicle, especially when manufacturers offer free trials of different services or those on a subscription.

The UN has already established a set of rules around cybersecurity and software updates. The WP.29 rules (R155 and R156) will come into force in the EU in July 2022 and will be mandatory for all vehicles by 2024. While many of these rules surround cybersecurity, they're also focused on "providing safe and secure software updates and ensuring vehicle safety is not compromised."

As well as safety and security around software updates, WP.29 will also cover these areas:

  • Managing vehicle cyber risks
  • Securing vehicles by design to mitigate risks along the value chain
  • Detecting and responding to security incidents across a vehicle fleet

Manufacturers will need to comply with these regulations for all features delivered with the vehicle, as well as those delivered via an update. While it will take time for these regulations to come into force fully, it's important that manufacturers take steps to ensure they are fully and satisfactorily adopted.

Insurance increases

Many insurers consider new features delivered via an OTA update to be a modification to the vehicle. This could lead to an increase in insurance prices or, at worst, render the cover invalid. We all know to report modifications to our insurer, but the rules around new features delivered over the air aren't quite so clear.

Recently, UK insurer LV did a U-turn on its policies after charging Tesla owners a premium following routine software updates. It told the consumer association Which?: "We now recognize that it isn't fair to expect customers to contact us for every update, so as a result of this valid challenge, we are changing our approach."

With no existing set of rules for insurers, each will decide its own approach to these updates. This could make life difficult for consumers and could impact how car manufacturers deliver updates in the future.

Vehicle Software Intelligence as a solution

While there may be challenges ahead for OEMs, the opportunities for revenue generation are too good to ignore -- especially in this rapidly evolving market. One solution that could ease the pain of these safety and regulatory challenges is artificial intelligence, specifically Vehicle Software Intelligence. This makes the update process more straightforward for car manufacturers by minimizing the size of update files, reducing costs, and giving accurate visibility of a vehicle’s entire software system -- supporting auditing and compliance efforts.

While software-defined vehicle manufacturers are leading the way when it comes to delivering OTA updates, legacy OEMs are catching up. In fact, more than 20% of industry experts expect software sales to account for at least 10% of carmakers' sales by 2027. The road may not be as smooth as some may hope but it's the early adopters that will reap the rewards in the years to come.

Find out more about how Vehicle Software Intelligence could help your business here.

Part 1 – Software will create profitable new revenue streams for OEMs

For many years, software has been an enabler for hardware, but, increasingly, it's becoming a source of revenue for automotive manufacturers. In fact, according to McKinsey, data-driven services could create up to $1.5 trillion in additional revenue for OEMs.

Profits on a new car are ridiculously tight, with many manufacturers making just 13-21% gross profit margin (GPM) on a car sale. These margins are squeezed even tighter thanks to supply chain disruption; increased steel, energy, and logistics costs; and more R&D spend. Compare this to the software industry, where the average GPM is 72.31%, and it's no wonder OEMs are turning to software to boost margins. Many are already using over-the-air (OTA) updates to deliver new features to vehicles even after the initial sale.

One of the most well-known examples of this is Tesla offering acceleration upgrades to its vehicles with a simple update -- for a fee, of course. But there's potential here for other brands to follow suit, and many already are.

Giving more to customers

Adding new vehicle features via OTA updates will not only drive revenue to manufacturers but can improve customer satisfaction too. Drivers can add new functionality to their vehicles as need dictates or if they want to customize a used vehicle to their needs. But this is so much more than updating satellite navigation or adding new infotainment features, software gives OEMs the chance to update the functionality of the car itself through firmware updates.

This often includes small upgrades to improve the performance of a car, as in the case of Tesla. The Polestar 2 is another example, however, it gained 67 horsepower from an update to the powertrain ECU, with a retail price of $1,125. This is a fantastic indication of what we can expect in the future as OEMs begin to use software as a revenue generation tool.

Tesla is used to monetizing these upgrades and does so with great success. For example, for $10,000, you can buy the full self-driving package for your car. Tesla makes this easy for customers and simply delivers these new features through an OTA update. This, essentially, activates the existing hardware enabling drivers to make use of Tesla's full suite of autonomous capabilities.

BMW is another manufacturer that's offering more to its customers through remote updates. Owners can choose to add a range of digital services to their vehicles -- either for a one-off price or a monthly subscription. You could add active cruise control, adaptive suspension, or BMW Drive Recorder -- this automatically activates in the event of an incident but can be used to record beautiful surroundings and road-trip memories at the touch of a button if you choose.

New revenue streams

It's not just BMW, Tesla, and Polestar monetizing these updates. Stellantis recently announced a strategy that will build on existing vehicle capabilities to transform how customers interact with their vehicles -- the company is predicting this strategy to generate 20 billion euros in incremental revenue by 2030.

Stellantis CEO Carlos Tavares said: "Our electrification and software strategies will support the shift to become a sustainable mobility tech company to lead the pack, leveraging the associated business growth with over-the-air features and services and delivering the best experience to our customers."

Manufacturers have plenty of options when it comes to monetizing functionality upgrades via remote updates. One-off fees add permanent features to a vehicle but the subscription model allows OEMs to create recurring revenue streams. General Motors is already using this to add new functionality to older models. Owners of around 900,000 vehicles built from 2018 can add navigation to their infotainment system for just $15 a month.

The opportunities are endless here, especially as manufacturers look beyond their infotainment systems and to the firmware of a vehicle. Updating the features of a car -- such as safety systems, performance, or self-driving capabilities -- is where the real money lies.

OEMs aren't shy about their plans to make money from these additional features. Markus Schafer, head of research for Mercedes cars, told CAR magazine: "We're aiming for an additional 1bn euro by 2025 to be added from packages and services that we're selling over the air. Of course, we want to provide features and new experiences to our customers, but also ultimately to do additional business in the future after we've sold the vehicle. That's going to be more and more important."

For consumers, OTA updates mean personalization for their cars, allowing them to add all the features they require to an otherwise standard used car. Adding these functionalities enables OEMs to continue earning from older vehicles while promoting brand loyalty among used car buyers. While software-defined vehicle manufacturers are leading the way, legacy OEMs are catching up. In fact, more than 20% of industry experts expect software sales to account for at least 10% of carmakers' sales as early as 2027. The road may not be as smooth as some may hope but it's the early adopters that will reap the rewards in the years to come.

 

Read Part 2 of this series here

Why car manufacturers are designing the software and not just the car

It used to be the mechanical details of a vehicle that made it stand out. Buyers wanted to know who had the best engine, which four-wheel-drive system was superior, or simply which was going to be the most reliable in bad weather. While these things still matter, times change, and OEMs are looking for new ways to differentiate themselves from the competition.

In the last decade, there's been a clear shift in the automotive landscape. We're seeing new propulsion types, the rise in autonomous abilities, and a level of connectivity that feels like it's straight out of science fiction. Consumers want to know if a car will park itself, whether an over-the-air (OTA) update will make it go faster, and which new advanced driver-assistance systems will keep them safe behind the wheel.

All these innovations rely on one common factor; software -- and for new energy vehicle (NEV) startups, in-house development has been crucial from day one. Legacy manufacturers are racing to catch up.

Software as a competitive differentiator

With the average vehicle containing around 150 million lines of code, the software makes up a large part of a car's value -- dictating new features such as gesture control, self-driving abilities, and voice interaction. With the likes of Tesla and NIO leading the way with software, many other automotive OEMs are looking for ways to bring their development in-house. This would not only improve time to market but also offer clear differentiation from competitors.

These changes won't happen overnight, though, as digital transformation of this scale takes time. Our recent Automotive Software Survey showed the majority of respondents predict that 10-25% of vehicle software will be produced in-house by mass-market manufacturers in 2025.

Consumers expect OTA updates

Most buyers think of Tesla when it comes to OTA updates for a good reason. The electric-only manufacturer has been building these capabilities into their cars since the launch of the Model S in 2012. Other automakers have struggled to keep up, though most now offer some form of basic OTA updates.

What is still setting NEV companies, such as Tesla and NIO, apart is the type of updates they offer. Most manufacturers can update the software on the infotainment systems but those leading the charge can also administer OTA updates to the safety-critical systems. This means being able to make adjustments and upgrades to more complex systems such as braking, steering and ADAS. Legacy manufacturers will struggle to do more than update the navigation and infotainment systems with their current development processes and OTA update solutions.

Data from Statista shows the value of the worldwide OTA update market could be as much as $7.5 billion by 2025, meaning it's not an area automakers can ignore. To keep up with consumer demand and not be left standing by NEV powerhouses, OEMs are looking for ways to quickly increase the capabilities of their OTA updates and launch new features. Bringing everything in-house is the clear solution but this will take time, meaning OEMs will continue to work with tier-one suppliers.

Managing the transformation

Using these suppliers is still necessary for most manufacturers but the benefits of in-house software development can't be ignored. It can help keep costs down, fast-track delivery, and protect against cyber vulnerabilities but there's a solution for OEMs who still need to outsource some elements of their software development: Vehicle Software Intelligence.

The key to working with suppliers is visibility. It's important to understand the bigger picture of inter-dependence and operability between elements developed by different vendors. Aurora Labs' Vehicle Software Intelligence is an AI layer that is used by OEMs in their software development efforts and the way they manage suppliers.

One challenge, for example, is software dependencies. When OEMs rely on third parties for their development needs, it's easy to lose sight of how different systems hang together. With a Line-of-Code Intelligence solution, manufacturers to get a better view of the system as a whole. This allows developers to keep an eye on the thousands of inter-related functions and capabilities to better understand the potential effects of new features and conduct OTA updates with confidence.

With 77% of respondents to our 2021 Automotive Software Survey stating that the trend towards in-house software will increase, it's clear that automakers have some work to do. The development of software needs to be treated as a strategic move by OEMs that want to stand out as the demand for software-defined vehicles continues to grow.

Want to learn how to apply Vehicle Software Intelligence to your software? Contact us.

At the Epicenter of the Unknown

For 10 years, I have been writing about and taking the bullhorn to the mountain to talk about automotive software and the benefits of over-the-air updates. For three years, I have been writing about and taking the bullhorn to the mountain to talk about automotive software and the benefits of validating what happens when there is an over-the-air update.

This week, I experienced what happens when an update is not validated and found myself at the epicenter of the unknown. I have a swanky new 2021 SUV. This model is no longer a boxy vehicle like previous models - it is sleek and fun and has many of the infotainment, ADAS and connectivity features we talk about on a daily basis in the automotive industry.

When I first bought the car, I could say, "Call Mike," and Mike was soon on the line. Now, I say "Call Mike," and I get the response - "Ok, let me help you with that. I need some more information. Look at the notification on your device."

"Looking at my device," forces distracted driving and is obviously not recommended. This prompt goes against every goal of bringing voice assistance into the car. I went to an online consumer OEM support group and read posts noting that this problem started in November 2021. With yes - an over-the-air update.

I'm sure the update did fix some things - or add some things - I don't know. I do know that the update screwed up my ability to call out by contact name (I can call out by dictating the phone number, but out of my 210 contacts, I know three phone numbers by heart.)

So, after going through many menus, I finally went to the dealer for help.

I met with a super nice support person. He tried - but his conclusion was that it was an Android Auto and phone problem and I had to go to AT&T.

I went to the AT&T store and met with a super nice sales person. He told me he wasn't certified to help me - he cannot give advice or guidance for anything in the car for liability reasons. He did give me a phone number for the AT&T Advanced Technology Group.

I called the AT&T Advanced Technology Group and another really nice support person told me that her group only works on networking issues to the car - hotspots and things. This AT&T person told me I had to talk to the car manufacturer and sent me to a really nice support person at the OEM who also told me I was again not talking to the right group and he forwarded me to another support group within the OEM.

Here is the kicker - I don't know if the next support person is really nice. My next conversation was with a phone recording repeating, "My name is Joe. I can't hear you. Please call back later."

I do love my new swanky, new SUV. This is my third purchase from this OEM.

I also come from phone company parents - so I'm sure the phone company helped to put me through college.

All of this really nice support and sales people are doing the best they can with the information they have.

I know that we are in the early days of 100 million lines of automotive software code. I also know that validating software behavior throughout the entire car resulting from an over-the-air update is paramount and that the really nice sales and support people from both the automotive companies and the service providers - need to be educated on how to help consumers navigate to success.

For now, I am still at the Epicenter of the Unknown. Please comment if you have any insights or fixes to this 'call by contact name' problem.

Three Reasons Why AI-based Vehicle Software Intelligence Solutions are Required

Vehicle Software Intelligence (VSI) is a category of solutions based on sophisticated AI algorithms that garner insight into the condition of, and interaction between, vehicle software assets. These solutions will be used throughout the entire lifecycle of the vehicle -- from the software development stage, through QA, production and on-the-road with over-the-air updates.

Vehicle Software Intelligence solutions help all who touch the software - from engineers developing the software to those running over-the-air software update campaigns - understand and act on software behaviour.

There are many use cases for Vehicle Software Intelligence solutions. Below are examples of the most pertinent use cases where VSI can help auto manufacturers today.

Understand software dependencies

According to a study conducted by Andreas Vogelsang of the Institut fur Informatik, Technische Universitat Munchen and Steffen Fuhrmann of the BMW Group, 1,451 dependencies were found between 94 vehicle features. With VSI, not only will you know which dependencies exist but more importantly, VSI analyzes the behaviour of the software functions and allows the OEM to know in real-time which connections and dependencies are active, which are not, where new dependencies have been created, and where existing dependencies are broken. Maintaining visibility into and a deep understanding of software dependencies is crucial for ongoing tracking, maintenance, regulations, security and new feature introductions.

AI-based Vehicle Software Intelligence solutions are required to understand the complex vehicle software systems and provide car makers with a clear, consistent and visible map of all software relations and dependencies.

 

Unused code detection

Automotive engineers that have been with their companies for more than 15 years often talk about how they find code they wrote 15 years ago still present in today's vehicles. In addition to this scenario, automotive software comes from multiple software Tier-1 vendors and the open-source community. This causes a major problem for a car manufacturer to obtain the Automotive Safety Integrity Level (ASIL-D) certification which states that there can be no unused code in a vehicle.

AI-based Vehicle Software Intelligence solutions are required to help track unused code for increased safety and for auto manufacturers to obtain Automotive Safety Integrity Level certification.

Evidence of software updates

By 2025, software is expected to reach 40 percent of the car value and based on a recent Automotive Software Survey, by the same year, it is expected that every vehicle will receive between 2 and 6 over-the-air annual software updates. Based on UNECE WP.29, in order for a vehicle to remain compliant with Type Approval regulations, the automotive manufacturer must document if the update is fixing bugs or a security patch, nullifying the need for additional certification testing. Another scenario is if the software update only affects a sub-section of installed vehicle software - limiting the amount of tests that need to be run to receive amended Type Approval.

AI-based Vehicle Software Intelligence solutions give automotive companies the solutions needed to prove what lines of code, and what features and functionality, have been affected by the software update making the process of remaining Type Approval certified streamlined and less expensive.

We have witnessed many industries go through disruption based on new technologies. Software is disrupting the automotive industry. It is changing the make-up of the required workforce, vehicle time-to-market and lifecycles, driver experiences, vehicle maintenance and the list goes on.

Vehicle Software Intelligence solutions are needed for the use cases mentioned above, in addition to cybersecurity simulations, memory and battery endurance and understanding and testing unpredicted scenarios. AI-based Vehicle Software Intelligence solutions will help the vehicle manufacturer obtain deep understanding of software behaviour to enhance the processes, reduce the cost and speed up software development, quality control, certification and over-the-air updates.

Vehicle Software Intelligence solutions are the key to the software-driven disruption of the automotive industry.