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