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.

With vehicles featuring more advanced driver assist systems than ever -- as well as autonomous capabilities -- it's no wonder software has become the most important element in a modern vehicle. As a result, automotive software is becoming more complex than ever, with more lines of code, modules, and components. This means the challenge of maintaining quality and reliability becomes even more pronounced.

Traditional software development practices are struggling to keep pace with the growing needs of today's vehicles. Many developers continue to use outdated methods, such as repetitive and time-consuming testing strategies, in an attempt to improve quality and reliability. The new era of software-defined vehicles demands an innovative approach to meet the need for comprehensive testing and rapid iteration.

Artificial intelligence as a quality assurance tool

Artificial intelligence (AI) tools are emerging as key allies in automotive software development -- especially when it comes to quality assurance. At the design level, AI tools can provide tested and proven blueprints, reducing the need to build every project from scratch. This significantly simplifies the development process and saves crucial time and resources. Furthermore, AI can provide a framework that maintains consistency and speeds up the development process.

When it comes to testing, AI's potential is equally transformative. These tools can identify specific tests to focus on based on the updated software functions and their interdependencies, which helps optimize test selection. The end result is a more efficient testing process that helps support the creation of reliable software and updates.

Aurora Labs' Auto Detect adds a layer of AI to the testing process to give actionable insights into which tests have the highest probability of failure. This means that when a new version is committed to the software repository, Auto Detect will optimize the order of test runs by selecting the tests to run first, ensuring a faster time to failure. This speeds up the software testing process while still ensuring all essential functions are tested.

The same tool can also improve quality by ensuring 100% test coverage. With hundreds of millions of lines of code and complex interdependencies throughout a single vehicle, manually trying to achieve 100% test coverage is a monumental task. Auto Detect uses Line-of-Code Intelligence to deliver detailed insights into the coverage of your tests, allowing developers to see what's covered by the testing scenarios -- and, crucially, what's not.

Culture change

In an industry as established as automotive, change can be difficult to implement, especially when it comes to ingrained development processes. However, the need for process innovation is as pressing as the need for software innovation. Traditional development models, such as the sequential production line model, are often inadequate for managing complex software projects.

An agile approach embraces small iterative steps and anticipates potential issues. Tools such as those developed by Aurora Labs are already proving to be game-changers in this area. For example, the automotive industry is familiar with the V-model development methodology, where customer requirements and corresponding tests form the two arms of the "V" with development at the base.

Traditionally, aligning these two aspects has been a manual task. However, AI tools can now read and understand documents, interpret the context, and map out the connection between the requirements, the software, and the corresponding tests. In this way, AI is not just working side by side with software development but is becoming an integral part of the process by identifying areas for improvement and ensuring compliance with regulations.

The road to reliable automotive software development lies in striking a balance between software innovation and process refinement. AI tools can undoubtedly catalyze this journey, but their success hinges on the ability of developers to weave these tools into an efficient, scalable, and innovative development process -- one that is prepared for the rapid changes defining the future of the automotive industry.

If you'd like to find out more about how Aurora Labs' AI tools can support your automotive software development, download the whitepaper here: Five software development challenges in automotive and how AI is addressing them.

The automotive industry is evolving at a rapid pace. Digital transformation, new revenue streams, and increasing automation and software are changing the way cars are built, bought, and driven. This means there are numerous opportunities for manufacturers and others within the industry to take advantage of.

A McKinsey report predicts that revenue within the automotive industry could increase to $6.7 trillion by 2030. This leap will be driven by evolving mobility offerings, digitization, and new business models. However, that doesn't mean there aren't challenges ahead. Whether it's the increasing safety requirements for autonomous driving systems, parts shortages, or changes in customer behavior, there are still bumps in the road ahead.

In order to solve these challenges, many automakers are turning to artificial intelligence (AI) to work around some of these hurdles.

Chip shortage

Analysts at AutoForecast Solutions predict the chip shortage will result in automakers building around three million fewer vehicles in 2023. While we are seeing supply chain issues begin to ease, the shortage of chips is still putting strain on manufacturers.

One possible solution is to reduce the NAND chip storage needed within a vehicle. Many current update solutions such as full-image or binary diff updates require dual partitions in the endpoint memory. This allows the update to write to the second partition so it can be rolled back to the previous version if it fails.

This not only requires double the amount of NAND chip memory but could also have a knock-on effect on other ECUs within the vehicle. If an update fails, other areas of the vehicle might need to also roll back, something that might not always be possible.

AI-powered technology, such as Line-of-Code Intelligence updates, can solve these challenges and reduce the amount of chip storage needed. This method of delivering software updates to the car requires fewer flash memory banks as it writes the fully executable update file to the next free space on the chip -- without deleting previous versions. This takes up much less space while also giving manufacturers the ability to roll back to any previous version of the software even without a second bank of flash memory.

Read more here: Could software be the answer to the chip shortage?

Safety requirements

With 94% of all road traffic incidents caused by human error, it's no surprise the European Commission has introduced new rules to increase safety. These new guidelines state that all vehicles will need specific advanced driver assist systems (ADAS) in place by 2024, which means automakers need the right software and technology to comply.

All vehicles will need to include:

• Intelligent speed assistance
• Reversing detection with camera or sensors
• Attention warning in case of driver drowsiness or distraction
• Event data recorders
• An emergency stop signal

Cars will also need other features such as lane-keeping assist and automated braking, while other types of vehicles have their own additional requirements.

These are all complex, interlinked systems that could pose a challenge for automakers as they develop new safety systems and software. To ease this and keep both quality and safety high, many manufacturers are turning to AI -- specifically Vehicle Software Intelligence (VSI).

VSI uses AI to map the interdependencies of each safety system. This makes it easier for developers to see how their changes might affect other functions. This hugely speeds up the time to market for the ADAS features and any future updates.

Read more: Safely certifying software-defined vehicles

Software recalls

Between 2011 and 2020, 331 million vehicles were recalled in the USA due to safety-related issues -- with the European market not far behind. In an increasingly competitive landscape with newer manufacturers such as NIO, Rivian, and Lucid challenging legacy manufacturers, this is a problem that needs solving.

AI can support developers as they build and test the software for a vehicle. This can lead to fewer errors and, as a result, fewer recalls. On top of this, AI-powered over-the-air updates can also make any bug fixes much more straightforward -- without requiring the customer to take their car to a garage or wait while it updates over the air.

Line-of-Code Intelligence technology maps all interdependencies to better understand the effect of any changes on other functions within the vehicle. This helps to eliminate recalls caused by updates to software that in turn, cause errors in other areas of the car

Small profit margins

The automotive industry is famous for its small profit margins but automakers are being squeezed by recessionary fears and a cautious market. While the recent increase in demand has allowed for profit margins to expand once again, the threats on incumbants from new mobility companies is once again threatening margins. This means manufacturers need to look for other ways to increase revenue.

Using a car's software to deliver new features isn't anything new -- take Tesla's Full-Self Driving feature ($15,000), for example -- but it's something legacy manufacturers have been slow to get on board with.

AI technology can support manufacturers in developing these new offerings as well as in the way they're delivered. A frictionless experience from order to update will ensure customers take advantage of new features such as subscription services, acceleration upgrades, and more -- all of which will provide a new revenue stream for manufacturers.

Read more: Challenges of using software as a revenue-generation tool

Artificial intelligence might not be the silver bullet that will fix all of the industry's problems but it has a chance to drastically ease some of the challenges faced by manufacturers. Solutions such as VSI and Line-of-Code Intelligence from Aurora Labs can help automakers optimise the processes required to realise new revenue streams, increase quality and weather supply chain shortages.

If you'd like to find out more about what Aurora Labs offers, contact us today.

On September 30, 1955, police, ambulance, and fire crews arrived at the scene of a horrific two-car collision at a desert intersection in Cholame, California. Medics found that the driver of the Porsche, rising star James Dean, had been thrown from the car and killed instantly.

Following an investigation into the collision, it was determined that Dean hadn't been wearing a seatbelt. If he had, he most likely would've survived the crash. This high-profile death raised public awareness of the importance of seatbelts and ushered in the age of automotive safety.

Not much changed in the coming years in terms of new safety features in vehicles but more people did start to wear seatbelts. It was only when electronic control units (ECUs) started to become commonplace that manufacturers began to introduce safety features such as ABS, ESP, airbags, and more recently, lane-keeping assist, Forward Collision Warning (FCW) and other advanced driver assist systems (ADAS).

History of the ECU

General Motors introduced the first electronics system into a vehicle in 1978. By 1981 all GM vehicles contained an engine control unit that helped manage fuel use and power within the vehicle. In the early 1980s, hybrid digital systems became popular with other manufacturers, too, these were able to measure and process inputs from the engine to yield preset output values. This ROM system, as it was known, was one of the first tunable systems.

By 1991 almost all US and Japanese manufacturers has abandoned carburettors in favour of fuel-injection systems controlled by microprocessors.

Now, all ECUs use a microprocessor that processes engine inputs in real-time. They're much more robust than other systems, especially as an engine begins to wear. As well as lending themselves well to tuning, these systems also enable more sophisticated safety features.

ECU-based safety technologies

Many vehicles now have a dedicated ADAS ECU that draws information from the vehicle's cameras, lidar and radar systems, and inertial measurement units, as well as map data. This enables various safety features, including:

• Lane-keeping assist
• Blindspot warning
• Adaptive cruise control
• Automatic emergency braking
• Hill descent control
• Lane change assistance

How to safely create an ECU

In order to power these advanced safety features, an ECU needs to be safe in itself. In 2014, for example, there was an incident where a software defect caused unintended acceleration with drivers unable to apply the brakes.

In order to avoid similar issues that could impact the safety of a vehicle, it's important that software is thoroughly tested. With more than 100 million lines of code and interdependencies that span every single vehicle system, this is too much for a human developer to work on alone. The use of artificial intelligence in the testing process is vital when it comes to obtaining 100% test coverage in order to uncover potential defects.

It's also important for developers to understand how each function in the software relates to one another. For example, a small change or update to the software controlling the braking system will affect more than how the car stops during normal driving -- it'll also impact ABS, emergency braking, and even adaptive cruise control, as all these systems are intertwined.

One of the main conclusions from an investigation performed by the Barr Group that led to the creation of a safety standard was that "testing is not enough to establish safety". Today, we understand that even these standards are needed to be enhanced.

Line-of-Code Intelligence is an AI-based tool that maps complex automotive software systems in order to understand the interdependencies of functions that might otherwise seem unrelated. This helps developers create safer ECUs from which to run advanced driver assistance features.

Regulating these complex systems

In 2011, the International Standards Organisation (ISO) created ISO 26262. This framework helps to identify the potential risks of software and hardware failure in a vehicle. As part of this, there are specific Automotive Safety Integrity Levels (ASILs) that can be assigned to a safety requirement and its potential hazards. These are determined by a series of classifications based on the likelihood of a hazardous event, the severity of a potential injury, and the controllability of a driver to prevent or mitigate that injury.

Depending on those factors, the safety requirement is given an ASIL ranging from A to D. The most safety-critical systems are ASIL D, and these have the most stringent testing requirements.

The latest version of ISO 26262 was released in 2018 and extended the scope from passenger cars to all road vehicles. Automakers were quick to adopt it in order to make their driver assistance systems as safe as possible.

Automotive safety has come a long way since James Dean's tragic crash, but there's still a way to go to ensure modern systems are as safe as can be. If you'd like to explore more about how artificial intelligence can improve automotive safety, find out more about Aurors Labs' technology here.

Automakers are delivering more over-the-air (OTA) updates than ever before as they strive to keep up with customer demand and enhance their User experience. Tesla has been leading the way in this respect with one OTA update roughly every four weeks. This is only set to grow, and many automakers are beginning to catch up.

In our new 2022 automotive software survey, we found that over 40% of respondents expect each connected vehicle to receive between two and six OTA updates per year from 2025 onwards and nearly 20% expect between seven and 12 OTA updates per year. This still might be a little way behind Tesla, but it's clear that even more traditional car manufacturers are leaning on OTA updates to deliver new features, fix bugs, and even improve a vehicle's security and safety.

The challenge for automakers, however, is the cost of delivering these updates. Our recent cost consideration guide found that the cost of full image updates could be as much as $2.7 billion when you take into account the cost of data transmission, cloud storage, and dual bank memory for a single large OEM (10m+).

Even though legacy binary updates require endpoint integration costs, they can help an automaker save money. We estimate the cost of these types of updates to be around $1.8 billion per year.

However, there's another type of technology that can reduce costs even further. Line-of-Code Intelligence offers a clientless solution that creates small update files, significantly reducing costs in three key areas.

1. Eliminating the need for dual memory

Even though costs have come down in recent years, the use of dual flash memory can soon add up across thousands of vehicles. It's possible to mitigate $1.7 billion in costs using Line-of-Code Intelligence technology to create the smallest fully executable update file possible.

Where other update types overwrite previous versions and require dual memory to allow for a rollback in case of issues, a Line-of-Code update simply writes to the next free space on the memory, meaning nothing is written over, and all previous versions remain. Not only does this help save on initial hardware costs, but can extend the life of the memory by reducing write-and-erase cycles.

2. Reducing cloud and data needs

Line-of-Code Intelligence technology uses AI and advanced algorithms to make the update files six times smaller than binary diffs. This means automakers could save around $73 million in data transmission and cloud storage costs. On top of this, the smaller file size also improves transmission times, which helps get OTA updates out more quickly.

3. Clientless technology

With Line-of-Code updates, there's no need to integrate proprietary software onto every ECU. Additionally, the updates use the same file format as the original embedded ECU file (ELF, S-Rec, Intel HEX); this guarantees all ECUs can receive the necessary updates without extra integration work.

This can reduce integration costs by as much as $1.4 million but also speeds up the development and delivery process by using technology and file formats that are already integrated into the toolchain. This removes the challenge of adapting testing, production, and maintenance systems.

For vehicle manufacturers that are starting to see the benefits of software as a revenue generation tool and those who are looking to better serve their customers, OTA updates will play a large role in the future. In our new 2022 Automotive Software Survey, we found that 62% expect up to 10% additional revenue for OEMs from selling software features OTA by MY 2027.

In order to manage the costs of these updates and create a more sustainable business model, automakers should look to AI and Vehicle Software Intelligence to help solve the problem of skyrocketing costs. If you'd like to know more about how Line-of-Code Intelligence technology could help save you money, get in touch here.

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.