There are three key drivers of team excellence: communication, customer focus, and continuous innovation. In the first two parts of this blog series (see part 1 / part 2) on unlocking team potential, we've explored the power of feedback and how strong customer relationships can improve product quality.

In this final part, we'll look at how continuous innovation is a key component for development teams looking to gain a competitive advantage.

Embrace failure as a learning tool

The most innovative teams are those willing to try new things. Even when those ideas don't pan out, there are still valuable lessons to be learned. The best teams aren't afraid to try new things and fail because they know it allows them to learn and improve. This promotes a culture of continuous innovation that comes from seeing any misstep as an opportunity rather than a true failure.

Customer-centric innovation

The best ideas don't always come from team brainstorming sessions. No one understands the market better than your customers and they should be a resource you tap into regularly. With a deeper understanding of your customers and their needs, it can be easier to find innovative solutions with the potential to disrupt.

With a focus on continuous innovation, you can bring this customer-centric approach into your monthly and quarterly workflows. Instead of turning to the customer when it's time to do something new, speak to them at regular intervals for feedback and insight. As their needs change and the market evolves, there could be new opportunities for innovation that would otherwise be missed.

Encourage cross-functional collaboration

While communication is important, taking this beyond the core development team is the cornerstone of innovation. When you add the expertise and insights of other teams within the organization, it's possible to innovate while improving productivity. Innovation thrives when different voices come together and these fresh perspectives could be the secret to your next breakthrough.

For example, working with the marketing and UX teams early in the development process can help set good foundations for the user experience. Encouraging this collaboration from day one can also mean fewer changes in the future. Equally, working with customer service teams could yield some interesting insights into customer needs and what they might be struggling with. This allows developers to deliver projects and fixes that solve those problems—taking a lot of the guesswork out of sprint planning.

The key here is to embrace the insights from different stakeholders. Their insights from different angles of the business can lead to a breakthrough during the brainstorming process.

Experiment relentlessly

Small, rapid experiments lead to big results. The key here is to give the team space to iterate quickly to test ideas, gather feedback, and pivot if needed. Working in sprints allows developers to experiment and adapt as things change. Making small changes during each iteration helps explore new value for the customer as developers build on the MVP (minimum viable product). This allows the team to explore new ideas while making other necessary changes based on priority.

This builds flexibility into how the team works and helps drive a culture of continuous innovation. The result is a product with proven value backed up by technology.

Nurture a culture of curiosity

Experimentation comes from curiosity so it's important to weave this into your company culture. Developers who are encouraged to ask questions and try new things will be the ones most ready to innovate—and often, the most enthusiastic about their work. Encouraging curiosity and exploration creates an environment where big ideas are born.

Innovation isn't a one-time event, it's something that needs to be nurtured on an ongoing basis. Encouraging this mindset in developers and giving them the space to experiment will result in disruptive ideas as well as innovative new ways of doing things.

Remember, the most innovative teams are the ones asking 'what if?'

In the first part blog post on unlocking team potential, we looked at the power of communication. This is a vital part of team productivity but great communication extends to the customer too. Teams that continually keep the customer's priorities top of mind are those who can deliver high-quality products that exceed expectations.

In this article, we'll look at how a focus on a client's needs can guide development teams and improve customer satisfaction.

Get to the bottom of the problem

What a customer says they need and what they really need aren't always the same thing. When identifying the key pain points that will drive feature development, it's important to make sure you're getting to the root of the problem. For example, customers might ask to reduce update size to speed up over-the-air (OTA) processes. However, one of the challenges lies in the downtime, where our technology offers a significant advantage—regardless of the update size. This capability could be even more valuable to customers than the update size itself.

Asking questions and listening to the challenges the customer is facing is vital. In this example, a developer might dig deeper into why the customer wants to reduce update size because focusing on downtime may be more effective. Taking this active listening approach leads to solutions that deliver real value for the customer.

Ongoing engagement

Your customers should be involved throughout the entire development process, not just at the end. They should be considered partners throughout the entire product lifecycle. Looping in customers early on helps to prevent surprises, catch any issues (or scope creep), and strengthen the connection between everyone involved.

Feedback is key

Every interaction with the customer is an opportunity to improve. While feedback between team members is important (as I talked about in part one of this series), it's also important to give the customer plenty of chances to give feedback on your progress. While this might seem like a way to hold things up, the time needed should be built into development workflows.

Even if the customer requests a change to the work done so far, this is going to be much easier to rectify than if it'd been caught later in the process. For example, if a customer needs to change the way a feature works, this is easier to complete before that feature has already been pushed to production and becomes tangled with other functions.

Gain valuable market insight

Your customers will likely interact with the market more regularly than your team. This means they'll have a deeper understanding of the current landscape and key challenges. Through regular conversations with your customers, you may be able to identify unmet needs, find new opportunities for innovation, or get a competitive edge by spotting emerging trends.

Innovative products aren't created in a vacuum. They're created in response to customer requirements, challenges, and pain points. To uncover what the market wants and what will best serve the organizations you work with, it's important to keep communication open and listen to those evolving needs.

Read our last part: Unlocking Team Potential: Continuous Innovation

As all developers know, there's pressure to balance speed, cost, and quality of projects. For those working in a high-tech, agile team, clear communication isn't just a nice-to-have—it's a vital part of any successful team.

In the first part of our series on unlocking team potential, we dive into the things that can boost team performance to consistently deliver quality code.

Align on the 'Why'

A successful team will always understand why they are doing something. Whether it's the reasoning behind a new feature or the overall objective of the project, everyone should understand the purpose of what they are working on. This allows team leaders to get proper buy-in from developers, as well as executives.

Speak one language

Speaking one language is about finding a common vocabulary to discuss the project at all levels. And it's not just developers who need to be on the same page but all stakeholders involved in the process. This means everyone understands the objectives, progress, and expectations.

Focus on MVP

It's tempting to aim for perfection but it's far more effective to define a minimum viable product (MVP) and then work iteratively on new features and updates. Proper communication during this process is vital as everyone needs to understand what the properties and trade-offs are. This helps the team avoid getting bogged down by perfectionism, allowing them to deliver value quickly.

Document, document, document

It's important to document as if your product depends on it—because it does! Clear, up-to-date documentation is the guiding force for every decision and task. This is important during the early stages of any project but becomes even more vital when it comes to delivery updates or fixing bugs.

Team leaders should properly communicate the importance of this at all stages. Give developers the space to include documentation duties in their usual workflows during a sprint to ensure these important tasks are completed alongside other tasks.

Feedback is a two-way street

Proper communication is built on a culture where everyone feels comfortable giving honest feedback in all directions. This fuels continuous improvement but also helps avoid roadblocks in the future. Build in time for developers to share feedback but make it clear that it's also welcomed at any stage during the process.

Listen

Your team, customers, and stakeholders could hold the key to innovation. This is why it's so important to listen. Direct feedback can be useful and it's important to take this on board but listen for the meaning behind all communications. A customer talking about a challenge they're currently dealing with could be an opportunity to add something new to a contract or a chance to go above and beyond for them.

The most groundbreaking products don’t come from the loudest voices—they come from teams that value communication and collaboration. When team members listen to each other’s feedback and understand the purpose behind their work, they can achieve even more together.

Read our next part: Unlocking Team Potential: Focus on the customer

In the early 2000s, the mobile phone industry underwent a shift, spearheaded by the introduction of the iPhone. This revolution wasn't just about hardware, it was the combination of software and user experience that set Apple apart. Today, a similar revolution is happening in the automotive industry, particularly among electric vehicles (EVs). This transition, initially dominated by Tesla, is now pivoting towards a new paradigm where automakers are focusing on the digital user experience as much as the physical features of the vehicle.

The Rise of Electric Vehicles: A Hardware-Centric Approach

The beginning of the mobile phone revolution saw a focus on the hardware. With more players in the market, it was important for traditional mobile phone manufacturers to focus on features and technology to stand out. This saw the introduction of slimline models, QWERTY keyboards, and later touchscreens.

The beginning of the EV revolution was akin to the advent of the smartphone. Drivers were interested in battery life (akin to mobile phone battery capacity), charging infrastructure (similar to network coverage), and physical performance metrics. Tesla, much like Apple, was a front-runner, not only for its battery technology but also for its ability to balance this with a unique user experience.

Chinese manufacturers, paralleling companies such as Samsung and Huawei in the mobile phone arena, quickly followed suit. They emphasized not just the hardware but also affordability, rapidly expanding the EV market's scope and accessibility. Our recent Automotive Software Survey showed that 31% of people would consider an EV from a Chinese manufacturer, with 21% of those stating the reason was that the price was attractive. 

This shows that the EV market is becoming more competitive with Chinese manufacturers able to compete on price in a way that others aren’t always able to. This means legacy manufacturers are looking for ways to increase their competitive advantage.

Software: The New Player in Automotive Innovation

As EV hardware matures and becomes more standardized, the distinguishing factor shifts to the digital user experience. This is determined by the vehicle’s software and how it serves the overall experience of the vehicle — an echo of the mobile revolution where iOS and Android defined user preferences.

The software in an EV encompasses everything from the intuitiveness of the infotainment system and the sophistication of autonomous driving features to the personalization of the driving experience. A vehicle’s software functions can be the thing that separates a good vehicle from a great one and users are beginning to pick up on this as they shop for their next car.

User Experience: Driving Customer Loyalty

In the mobile industry, Apple's success was due to its product and ecosystem. The seamless integration between hardware, software, and services (like the App Store, iCloud, etc.) created a loyal customer base. A study found that Apple has the most loyal customers — 92.6% of iPhone users plan to stick with Apple for their next phone, compared to 74.6% of Samsung users.

In the automotive world, a similar trend is emerging. Manufacturers are not just selling cars; they're offering a holistic driving experience that extends beyond the vehicle. This will only increase loyalty as drivers get used to the user experience of their favored brand.

This all comes down to functionality such as over-the-air updates that refresh the vehicle's capabilities, apps that control the car’s features remotely, and even subscription-based services that unlock additional features. The focus is shifting towards creating an ecosystem where the car is an extension of the driver’s digital life.

Some automakers are moving away from siloed third-party systems such as Android Auto and Apple CarPlay and are instead focusing on creating intuitive native infotainment systems. While consumers might want easy integration with familiar services, this shouldn’t come at the expense of the in-cabin experience.

Automotive strategist and influencer James Carter recently spoke about this on LinkedIn, praising Rivian and Tesla for their infotainment systems. He said: “Both took the time to develop a ground up solution that is fully integrated with other features, such as Supercharger location details, ideal charge time and alternate route ideas. Everything you need is right there on the screen. What’s more, the maps are fast and the overall experience is seamless.”

According to our Automotive Software Survey, 40% of automotive professionals feel there’s going to be a shift in the industry to embrace Tesla-like continuous quality processes within five years. However, many manufacturers seem to be struggling to match what Tesla has been able to do. In 2022, 55% of automotive professionals thought the shift would come in five years. Now, more respondents than even think this will come within 10 or 15 years.

https://www.auroralabs.com/2023-survey-results/

Challenges and Opportunities

This shift isn't without challenges. Traditional automakers must adapt to a software-first approach, which differs from their traditional mechanical expertise. This opens up opportunities for new players, much like the mobile revolution, where many traditional phone manufacturers couldn't adapt to the smartphone era.

With software as a competitive differentiator, many automakers need to balance new hurdles with the traditional challenges of automotive manufacturing. Fisker, the US-based EV maker, filed for Chapter 11 bankruptcy at the end of June 2024. While the company intends to keep serving existing customers and building a network in Germany, there was a critical issue with its software 3.0 deployment. The update compromised several large data volumes, which frequently drained the vehicle’s small 12V battery.

For other automotive professionals, safety is, as ever, a big concern with 27% of respondents to our Automotive Software Survey stating that ensuring safety and reliability is one of the most challenging elements of automotive software development. Furthermore, data security and privacy are paramount, just as they are in the mobile industry. Consumers will demand transparency and control over their data, and regulations will likely follow.

The automotive industry is at the cusp of a revolution that will change how people buy cars. The companies that will dominate this new landscape are those that will understand the importance of software and user experience. They will be the ones to create not just vehicles, but holistic, connected, and personalized driving experiences. Just as Apple reshaped our perception and use of mobile phones, we await the visionary companies that will redefine our concept of the automobile in the era of electric vehicles.

As the world advances in the age of artificial intelligence – particularly generative AI – it might feel as if there are androids among us. Artificial intelligence that can generate words and pictures, understand and respond to conversations, and perform tasks is a crowning achievement. At least it feels that way now, only time will tell where things will go next.

When we were children, we were always asked: “What do you want to be when you grow up?” There was always a wide range of answers – one wanted to be a veterinarian, the other an astronaut, and so on. But if we could ask AI this question, what would it say?

Perhaps one of the most iconic AI characters is Lt. Commander Data (played by Brent Spiner), from the TV series Star Trek: The Next Generation. In it, Mr. Data helps with calculations and problem-solving, all with the speed and accuracy of an android. What often lets him down, however, is that he’s unable to understand and master human emotions. While his perception and access to information are all huge strengths, Data wants something incredibly human.

AI in Automotive Today

Today, artificial intelligence is one of the hottest topics in the world and it’s giving us an idea of what AI might want to be when it grows us. The automotive industry is embracing this technology in everything from its production line to vehicle software. It starts with the adaptation of in-car systems, such as ChatGPT by VW, Tesla’s AI-powered Autopilot, or the MBUX infotainment by Mercedes. There’s no doubt that, in time, it will advance further into our vehicles and to more safety-critical elements, as well as into human-car interaction, route-planning, and more that, right now, we can only dream of.

Here are some of the areas AI is already being used in the industry:

1. Production line:

   1. Robots that build vehicles and are able to detect defective materials
   2. Parts warehouse management robots that AI in use sensing and routing

2. Accident prevention:

   1. In-cabin driver awareness - falling asleep, DUI, distractions off-road 
   2. Pattern learning of dangerous driver behavior
   3. Obstacle identification
   4. Adaptation of car limitations according to weather conditions

3. Maintenance: 

   1. Preventing car breakdown on-road by learning symptoms in advance.
   2. Car smart usage - reduce wear and tear.

4. Fleets:

   1. Learning and plotting optimized distribution routes
   2. Detecting and identifying defects for car rental companies

In addition, here are three AI-driven areas of specific interest to me:

Autonomous Vehicles

You don’t get very far in a conversation about AI in automotive without touching on autonomous vehicles. 

There are five levels of automation with level one vehicles being able to handle single tasks such as automatic braking while level five is fully autonomous capabilities without the need for driver presence. 

Today we are at level 2 with advanced driver assist systems (ADAS) providing accident prevention capabilities such as forward collision warning (FCW), lane-keep assist, adaptive cruise control, and more. This enables independence but still needs to be monitored by the driver.

To achieve this, the vehicle needs to “see” the world outside and understand it. It does this through cameras, LiDar, radar, IR sensors, and more. With all this information, the vehicle can make small decisions such as keeping the car in the lane if it drifts out of the white lines.

As well as good hardware, this is only possible with the right software to accompany it, otherwise the vehicle won’t know what to do with the information the sensors and cameras are feeding it. 

The world is aspiring to get to a point where all vehicles are fully autonomous (level five). This would mean all cars talk to one another through Vehicle-to-Vehicle communication (V2V) and the infrastructure around them through Vehicle to Everything (V2X). Once we get to this point, all you’ll need to do is get in a car, tell it where you want to go, and let the AI do the rest.

Vehicle Insurance 

What if an insurance company could adjust insurance fees according to the behavior of the driver?

Usage-based insurance looks at your behavior as a driver and adjusts the price accordingly. This means safer drivers will have lower premiums than those considered more at risk. Previously things like black-box insurance have made this possible, but now insurers are exploring AI to facilitate this. 

According to McKinsey, 10% to 55% of roles within insurance could be replaced by AI in the next 10 years – particularly underwriting, claims, and finance. In the future, almost all claim and fix processes will be managed by AI, reducing human involvement to the minimum, and maybe even reducing the costs for us consumers.

Just like autonomous vehicles, this requires sophisticated software to be successful. However, as insurance companies are dealing with sensitive data, security is paramount. All the details of a driver need to be aggregated to a server to help teach the AI and inform its outcomes. Disregarding errors and security risks in the software could lead to noncompliance, legal issues, lost revenue, and poor brand reputation.

Predictive Vehicle Maintenance

When a car breaks down, there's nothing to do but fix it – sometimes making the car unusable and maybe even stuck somewhere, something that is extremely expensive for commercial fleet companies. But what if we could know what’s going to become an issue before it breaks? Proper maintenance of a vehicle will always help to keep breakdowns to a minimum but AI can take this to the next level with Predictive Maintenance.

This is especially useful across fleets where keeping track of each individual vehicle can be challenging. AI can study how each vehicle is being used, monitor driver behavior, and begin to learn trends that could contribute to breakdowns. This will ensure fleet managers can minimize downtime while keeping on top of vehicle maintenance.

This technology can also take some of the unknown out of purchasing a second-hand car. With AI, the buyer could validate the health of the vehicle and see if any major breakdowns are around the corner. This can help them make a buying decision and potentially save a huge amount of money when looking for an affordable used vehicle.

In the future, we will see completely automated maintenance where the car will not need to get to the garage at specific times in the life of the vehicle, and the entire BOM (Bill of Material) for the car’s maintenance will be known ahead of time, lowering storage needs and enabling more efficient garage working hours.

Throughout the years, Mr. Data may not have been able to master human emotion but came close to learning to mimic this ability in his own way – or, of course, use a very buggy emotion chip. AI will probably be the same. It might be a good replacement for a lot of human tasks and maybe even perform better in some cases, but there will always be a limit.

During Star Trek: TNG, even with Mr. Data’s great programming, he still was prone to bugs and misuse of his abilities. According to Star Trek, computer bugs and cyber threats are still a real problem in the 24th century, and we have no reason to doubt the logic of the show’s writers. AI has real potential but it has to be used in a way that plays to its strengths.

Aurora Labs has developed an LCLM (Large Code Language Model)  that works at the line-of-code (LOC) level at runtime, this enables us to identify deviations and anomalies at a very basic level that can discover not only coding bugs but software functionality misbehavior as well. It can monitor the software in real-time to detect changes in the software’s behavior before these escalate to become critical system errors. Raising a flag before a system fails will not only ensure that the devices continuously learn and improve but could also save lives in devices such as cars or trucks. 

While emotions may be a step too far for AI-based devices, self-healing is one form of human nature that I truly believe can be achieved.Find out more about Aurors Labs’ technology here: https://www.auroralabs.com/product-overview/

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

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

This is part one of our series.

Automotive software is undergoing a technological shift. With evolving architectures and ever-increasing customer expectations, the traditional tools and methodologies that once dominated are now being challenged. As we move to the era of software-defined vehicles, there's a pressing need for a new generation of tools that can keep pace with these changes.

This is the first article in our three-part series on AI software development tools, where we explore how this technology impacts the automotive industry.

V-shape development and AI's role

Historically, the automotive industry has relied on the V-shape development model. This model begins with customer or OEM requirements on one side and culminates in tests to ensure these requirements are met on the other. Traditionally, this process was manual, involving extensive document reviews to ensure tests aligned with requirements.

However, with the advent of AI, this is changing. Large Language Models (LLMs) can now read and comprehend these documents, understanding their context. By training AI on these requirements, it can bridge the gap between what the customer needs and the testing process. This approach not only streamlines the process but also ensures a higher degree of accuracy.

This is an area early on in the development process where AI can have a significant impact. With an understanding of specific requirements, this technology can ensure that the software fits those customer needs at every stage of the development process. This speeds up the time to market by ensuring the project is on track at all times. Without AI traceability, there’s the risk of developers producing software that’s not quite fit for purpose, which could lead to additional time needed to bring the code in line with the original requirements.

Limitations of traditional tools

The traditional approach to updating embedded software in vehicles is cumbersome. For instance, traditional update methods require the previous version of the software to be completely erased in order to make space for the latest version. Given that modern cars have more than 100 ECUs, this method becomes problematic, as well as time and data-intensive. This also makes it difficult to roll back to a previous version should the latest software cause an issue within the vehicle.

The industry's reliance on tools that build these embedded software images is a significant limitation. However, newer technologies are emerging that allow for updating only the changed parts of the software. This approach, while requiring the integration of new tools into the CI/CD process, offers a faster and more agile way to update software.

Aurora Labs’ Auto Update technology creates the smallest possible update file to be written to the next free space on the existing flash memory, eliminating the need to overwrite the existing software version and enabling instant rollback if needed. Utilizing AI and advanced algorithms means update files are 6x smaller than alternative differential technologies, directly affecting data transmission and cloud storage costs. In addition to the need for remote software updates during aftermarket service, there are great time and resource efficiencies to be realized during the product development and system testing (/pilot vehicles) stages.

AI's transformative potential

The potential of AI in revolutionizing automotive software development is vast. For many developers, AI is becoming more than an add-on, it’s an integral part of their toolkit. They see opportunities where AI can be used to develop and test code, bringing fresh perspectives and methodologies to the table.

The automotive industry stands at a crossroads. With the rise of software-defined vehicles, the tools and methodologies of the past may no longer suffice. Embracing new tools, such as those that use AI, is not just beneficial; it's essential. As the industry continues to evolve, those willing to innovate will lead the way, shaping the future of automotive software development.

If you’d like to learn more about the Aurora Labs suite of artificial intelligence tools, get in touch today.

Part 2| Part 3

Commercial vehicles have a wealth of different requirements compared to passenger vehicles. Whether that's due to their size, the miles they need to cover, or the complexities of using autonomy in a logistics setting, there's a lot of work going into future-proofing these vehicles.

Remarkable innovations are taking place in the world of commercial vehicles, driven by their data collection capabilities and the unique challenges they face. In this article, we'll dive into some of these advancements and what that means going forward.

Driver-assist systems

Just as in passenger vehicles, trucks are fitted with advanced driver assist systems (ADAS). For the most part, these are similar to those in cars with features such as lane-keeping assist and collision mitigation to help improve safety.

However, because trucks are larger and heavier than passenger vehicles, they need additional safety features such as brake hold mode to avoid driver fatigue during long periods of standstill, and auto hold, which is exclusive to the Freightliner Cascadia. This feature actively brakes the truck to a safe stop in its lane rather than letting the truck roll to a halt in the event the driver is incapacitated.

In the future, these features will become even more sophisticated with improvements in both hardware and software, allowing trucks to identify hazards from further away. This will help to improve visibility and safety for truck drivers and the other road users around them.

Autonomous driving and platooning

We're closer to an autonomous truck than we are to full driverless cars. The reason is that commercial vehicles spend most of their time on the highway, which is a much less complex environment when it comes to the road markings, what's around, and the types of maneuvers one might want to make. Compared to a passenger vehicle that might be navigating tight city streets or narrow country lanes where other vehicles, pedestrians, or animals might seemingly come out of nowhere, there's a lot of predictability in driving on the highway.

Many of the tests on autonomous trucks are still being carried out with a driver to take over should something go wrong but we're closer than we've ever been. With new hardware innovations that make the most of LIDAR, cameras, and radar systems, combined with more powerful ECUs, we could see driverless trucks traveling for 24 hours a day. Something a human driver could never do.

While this might not be the reality just yet, there have been some promising results from truck platooning trials. This is where a convoy of trucks is able to follow one another closely to reduce air drag, improve fuel economy, and free up space on the roads. This is automation rather than full autonomy, though, as drivers are still needed when the vehicle needs to break from the convoy and continue to its destination.

Predictive maintenance

The use of artificial intelligence enables fleet managers to get a better sense of when maintenance might be required on a commercial vehicle. Using sensors within the vehicle, as well as AI tools, the maintenance needs of a truck can be accurately predicted. This avoids the need to send technicians into the field, and instead, allows vehicles to be worked on when they are back at base.

By predicting the maintenance needs of a vehicle, fleet managers can ensure those routes are covered by other trucks in order to minimize downtime. On top of this, handling maintenance in this way also improves safety. Tire sensors combined with predictive maintenance algorithms could help avoid blow-outs, for example.

Software innovations

While hardware is important, it's the software that runs all these smart features. This makes commercial vehicles incredibly complex, so software innovation is needed to ensure the systems run without issue. 

Software development tools are needed to ensure that system integration is validated throughout the truck's lifetime; OTA updates should be performed with zero downtime and without interrupting the truck's productivity; and continuous software behavior monitoring should also be performed, even while the truck is on the road. This ensures any system malfunctions are detected before they cause vehicle downtime.

Using AI is a vital part of this development and maintenance process and can help detect changes in the software's lines of code, behavior, and relationships within a vehicle.

This not only speeds up the development process but can also improve the time to market for updates and additional features. Aurora Labs' Vehicle Software Intelligence helps solve some of the challenges of developing software for commercial vehicles -- both now and in the future. If you like to find out more, book a demo here.

Automotive software is a hot topic at the moment, and rightly so. Everyone from modern electric vehicle companies to legacy manufacturers are discussing the challenges and opportunities of software.

In a recent interview with Fully Charged, Jim Farley, CEO of Ford Motor Company, spoke about the difficulties in getting software right. He highlighted the issues with multiple software providers and the lack of integration between them, something that caused Ford to bring the development of its electric architecture in-house.

"It's so difficult for legacy car companies to get software right," said Farley. "The problem is that software is written by 150 different companies and they don't talk to each other. There are different software programming languages, the structure of the software is different, it's millions of lines of code, and we can't even understand it all. That's why at Ford we've decided to completely insource electric architecture and to do that, you need to write all the software yourself. But just remember, car companies haven’t written software, they've never written software, so we're literally writing the software to operate the vehicle for the first time ever."

It's not just Ford that's facing these issues, but Tesla too. In a Twitter Spaces interview, Farley spoke to Elon Musk about software. Even though Tesla is the leading software-defined vehicle manufacturer, Musk admitted: "Automotive software is super hard."

Stellantis isn't immune either. " has gotten too complicated, too expensive, some of the cars you get in and I have to ask someone how to start it," said Ned Curic, CTO of Stellantis, during a talk at Innovation Day for CEA-Leti in Grenoble.

Stellantis, which manufactures cars under the Citroen, Fiat, Peugeot, Alfa Romeo, Opel, Dodge, Jeep, and Vauxhall brands, is all too aware of how complex modern cars have become. During the same talk, Curic explained: "We need to figure out how to do more with less. We eliminated 150 features out of 250 in the cabin . We have 270 silicon devices in a vehicle -- we have shrunk that to 70."

It's not just a handful of brands putting this level of thought into their software. A recent report from Reuters Events showcased the consistent focus on connected and software-defined vehicles. Almost 60% of its conference attendees said they were interested in this area.

The report also highlighted how many larger manufacturers are keeping their software development in-house. Markus Duesmann, CEO of Audi explained how external collaboration on software isn’t on the cards any time soon. He said: "At the moment, it would take away speed and add complexity. We are big enough to cooperate with ourselves and to have enough scalability."

Toyota's new CEO, Koji Sato, is also focusing on software. During an announcement back in April, he said: "Connecting the latest hardware and software will enable cars and various software applications to freely connect. Arene will fulfill an important role as a platform to support this kind of evolution. We will do our utmost to develop a next-generation BEV for 2026 together with Woven By Toyota."

Other brands are working on their own software platforms too. Mercedes-Benz has a new MB.OS infotainment system that will link to other areas of the vehicle and will be built around partnerships with tech firms such as Google and Nvidia. "We are dedicated to building the world's most desirable cars," Mercedes CEO Ola Källenius said. "We made the decision to be the architects of our own operating system -- a unique chip-to-cloud architecture that leverages its full access to our vehicles' hardware and software components."

AI creates actionable insights

The comments from these industry leaders all illustrate the need for solutions that address the challenges manufacturers are facing. AI-based tools can help overcome many of these hurdles from requirements to coding to continuous integration and continuous deployment (CI/CD).

One way automakers can speed up the development process and get updates out more quickly is with AI tools, such as those from Aurora Labs. This speeds up everything from development to testing to deployment by adding focus and traceability to the software development lifecycle.

The ongoing quest to solve these challenges presents an exciting opportunity. Industry giants like Ford, Tesla, Mercedes Benz, VW, Toyota, and Stellantis are all engaging with the issue head-on, each recognizing the value and the challenge of insourcing software development. The key to success for car brands -- whether they tackle their software in-house or outsource it -- is understanding the role AI-based tools play in the software development and maintenance process.

With the ever-present necessity for innovation, every player in the automotive industry must consider the role of software as an integral part of their future. Leveraging AI for more rapid and efficient development cycles will be key to staying relevant and competitive in an ever-evolving market.If you'd like to find out more about how AI can solve the big challenges facing manufacturers, download the whitepaper here: Five software development challenges in automotive and how AI is addressing them.