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/

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

On a complex piece of automotive software, testing newly deployed code is vital. With so many interdependencies between each function, those tests ensure any changes don't negatively affect other areas of the system. However, with the amount of code and its complexity increasing with every build, running every single test for every version of the software can take hours.

While this is a necessity for the system software before it is released for production or before OTA updates are to deployed, running all the tests for daily builds while still in development is hugely inefficient -- especially if only a specific function has been updated.

Identify all affected systems

Even when a new version of the software seems to only update one specific function, it's important to identify which other systems could be affected. The Line-of-Code IntelligenceTM technology developed by Aurora Labs maps all interdependencies to better understand the effect of any changes on other functions within the vehicle.

Eliminating test execution redundancy

To identify these dependencies, Aurora Labs' Auto Detect adds a layer of artificial intelligence (AI) to the software testing process. This helps to detect exactly which tests are needed for each software function, enabling the tester to focus on and only run the required tests, eliminating the need to re-run all tests every time a new version is committed to the software repository.

For example, if a system has 100 tests, Auto Detect will be able to select the right ones based on exactly what's been changed by an update -- including which other functions might be affected by the new code. This means that of those 100 tests, only 20 might need to run. This saves valuable time by ensuring only the necessary tests are performed.

By adding this AI layer to your development process, it's possible to decrease the time it takes to run tests. On top of the increased efficiency, you improve quality by ensuring dependent functions are also tested alongside those that have been directly updated -- without the need to run redundant tests.

If you'd like to find out more about how Auto Detect can improve the automotive software testing process, find out more here.

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

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

Innovation in automotive right now

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

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

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

Software-defined vehicles

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

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

Challenges

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

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

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

How to build a culture of innovation

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

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

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

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

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

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

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

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

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

The shift left

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

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

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

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

Testing the modern vehicle

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

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

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

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

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

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

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

Understand software dependencies

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

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

Unused code detection

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

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

Evidence of software updates

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

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

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

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

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

A watershed describes an area of land that contains a common set of streams and rivers that all drain into a single larger body of water. History shows that for new technologies to be successfully deployed, there needs to be a watershed moment where everything from powerful chipsets, to advanced networks, standards and interoperating components need to come together at the same time to truly leverage the power of new solutions and change the way people live, work and play.

This reality was recently reported in The Wall Street Journal's Decade of Disruption supplement. In Joanna Stern's article, First the Smartphone Changed then Over a Decade, it Changed Us, she quotes AT&T's former Vice Chairman, Ralph de la Vega, saying "When one piece of technology changes, it's a big deal, but when two or three things change that are complementary at the same, it's really disruptive."

For the mobile phone industry, only when the network technology (3G), operating systems (iOS and Android) and the phones' processing power, battery life and storage capacity reached a tipping point did mobile phones become ubiquitous to our way of life with consumers watching videos, receiving mails, playing, shopping, banking and surfing online all from the computer in their hand. This watershed moment also made over-the-air (OTA) updates truly beneficial for smartphone users. At this point in time, all of the firmware and applications - the entire phone - could be updated and continuously maintained throughout the lifetime of the phone.

Once OTA solutions were ubiquitous within the mobile phone industry, the OTA vendors set their sights on the automotive market. The car became the next device to update. Today, most of the over-the-air updates are keeping the non-critical head unit, infotainment system and telematics control unit updated. With the exception of Tesla, that built their vehicle platform as a software platform from the ground up.

New technologies comprising today's watershed moment for software as the predominant technology in cars -- the technology rivers and streams that are aligning -- are electrification, 5G, computer vision and software solutions based on advanced AI and machine learning. This convergence will ensure that automotive manufacturers can update more than the head unit, infotainment system and telematics control unit. This watershed moment will result in the ability to run software diagnostics and updates for the safety of critical and non-critical components alike throughout the entire vehicle - end-to-end, bumper-to-bumper, hood-to-trunk, bonnet-to-boot.

This watershed moment also offers the opportunity for automotive manufacturers to redesign their entire E/E architecture and adopt new centralized systems that manage in-vehicle software components as micro-services, ensuring flexibility, cost savings, software quality, safety and security.

With all the excitement around autonomous vehicles lately, you would expect that consumers are ready to buckle up and embrace full autonomy. On the contrary, there is a surprising amount of caution amongst drivers when it comes to self-driving cars.

According to a study conducted by AAA last year, 73 percent of American drivers report that they would be too afraid to ride in a fully autonomous vehicle - and that number is up from 63 percent in 2017. Furthermore, that same study found that 63 percent of U.S. adults reported that they would feel less safe sharing the road with an autonomous vehicle while walking or riding a bicycle.

How can we expect the entire world to adopt autonomous vehicle technology if there are high levels of distrust within the U.S. alone? Trust is a crucial component - and the first step - when it comes to the widespread use of autonomous vehicles. Autonomous driving will only become mainstream if drivers and pedestrians feel that they can fully trust the vehicle to function correctly.

What we can learn from the past - Trust and Technology

It is interesting to look at how elevators transformed the way people get from the first floor to the penthouse and how autonomous vehicles will transform how people get from point A to point B on the highways and back roads.

There are similar "trust" factors regarding both elevator and autonomous vehicle adoption. The elevator technology itself was not the deciding factor for people to adopt elevator usage. The first elevators were installed in England in the 1830s. However, early elevators used rope- and belt-driven systems that would often snap resulting in injury and death.

It wasn't until 1854 - 24 years after the technology was introduced - when Elisha G. Otis introduced a safety brake using a spring action system. At the 1854 World's Fair in New York City's Crystal Palace, Otis rode the elevator intentionally severing the cable. The safety brake stopped the elevator. The brake is considered the key in gaining public confidence in elevators.

We are in a similar situation today with autonomous vehicles. The major automotive manufacturers, major technology companies and hundreds of start-ups are working on technology to make autonomous vehicles a reality. Key to the adoption of the autonomous vehicle will be the technology that keeps the software safe and guarantees a driving experience that people trust.

Self-Healing Software Delivers Trust

This trust in autonomous vehicles starts with drivers understanding and believing that the software in the car works correctly, is secure, and is safe from cyber-attacks. Only then will the masses adopt self-driving vehicles. That means that the software must be functioning to the best of its ability.

The well-documented growth in the amount of automotive software eliminates any question about the importance of automotive software management. Using new technology, including artificial intelligence and machine learning, is required to deliver a Self-Healing Software solution that can detect a software problem, automatically roll-back to a safe software version and efficiently update the software.

Technology changes and advances - the safety mechanisms required for people to adopt technology - are timeless. Learning lessons from 1854, we know that trust is a key element for technology adoption and Self-Healing Software is to the vehicle what the brake was to the elevator.

Earlier this month, SAE International announced that it is joining forces with Ford, General Motors, and Toyota to create the Automated Vehicle Safety Consortium (AVSC). This consortium will look to create industry-wide safety standards for autonomous cars - something the industry certainly needs as the technology within it expands exponentially. The AVSC will also work with other organizations around the globe to develop these industry standards.

This consortium is excellent news for the automotive industry. Assuming that the AVSC recognizes the importance of maintaining the quality of the AV software throughout its lifetime and mandates that OTA updating be part of any AV system, our In-Vehicle Software Management solution could help OEMs in meeting those requirements. The possibilities are endless in terms of how our software can play an important role in what the AVSC is trying to accomplish.

And when we got to thinking about it, we realized that there are two common goals between the AVSC and Aurora Labs that will be crucial in moving this industry forward: safety and trust.

Safety is at the heart of this consortium - just as it is at the heart of our technology. Similar to how AI is at the core of the AV technology, here at Aurora Labs we believe that it is only natural that AI will also be used to autonomously cure vehicles from malfunctioning software, whether malicious or incidental. Self-Healing Software can enable vehicle software to detect anomalies in the software behavior and health and either on command or autonomously recover to its last known secure, certified and functional version without any downtime. This ensures that the AV stays functional at all times, creates a seamless experience for the driver/passengers and ensures the car is safe from any software anomalies.

Not only that, but trust largely underlies the goal of this consortium. In order for the innovation of autonomous vehicles to move forward, our society needs to trust that they will always work as advertised, safely and securely. Having industry-wide safety standards will help build this trust in consumers. Similarly, a goal of our Self-Healing Software is to build up driver trust in self-driving cars. Drivers must have faith that the technology in cars is continuously operating safely and securely before these vehicles will become widely adopted and commonplace in our society.

These shared goals ultimately come down to one focus: the people on the roads, whether they are behind the self-driving wheel, passengers or pedestrians. The AVSC is hoping to set industry-wide standards to make sure drivers have the most optimal, convenient and safest experience possible with self-driving cars - and this naturally also translates to pedestrian safety.

We are in the midst of an extremely innovative and exciting time for self-driving cars. Here at Aurora Labs, we're looking forward to seeing what will come from this consortium and the positive impact it will have to make truly autonomous mobility a reality.