AI insights increase software quality in the automotive industry

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

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

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

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

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

The shift left

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

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

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

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

Testing the modern vehicle

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

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

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

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

The Role of AI in Software-Defined Vehicles

When most drivers think of artificial intelligence in their vehicles, they think of the sensors and cameras feeding automatic safety systems or allowing for some level of autonomous capability. There is, however, a use for AI throughout the entire vehicle software architecture.

Modern cars have evolved significantly in the last few years and it's not unusual for a car to run on 100 million lines of code over 100 or more ECUs. All the safety systems, entertainment features, drivetrain, and interior have a raft of inter-dependencies. This means that if something goes wrong in one system, it could have a ripple effect of errors throughout the vehicle - something that's hard to predict during the development stage.

Enter Vehicle Software Intelligence

To solve this problem, Vehicle Software Intelligence (VSI) uses AI to better understand and map these complex systems. This gives developers a better understanding of how the different software elements in a car link together and behave but it also provides a wealth of opportunities when it comes to over-the-air (OTA) updates and continuous development.

The Guidehouse Insights' whitepaper Vehicle Software Intelligence - Adopting the Artificial Intelligence Required to Create a Software Defined Vehicle, explores how the automotive market has changed and how VSI is needed to drive new innovation within the industry.

One area the report explores is the web of dependencies within a vehicle. It states: "With the vastly more complex interactions of today's vehicle systems and what is yet to come, VSI tools that can see across all of the domains and run AI algorithms to map the software functionality and behaviour will detect potential conflicts."

Unlike traditional methods, Vehicle Software Intelligenceunderstands the intent of the software, the intricacies of systems that vary widely in function, their behaviour in real-time, and their interdependencies - something it's near-impossible for developers to do manually with static code analysis tools. On top of this, VSI also sets the groundwork for a new way of working in the industry, with many manufacturers moving to a cycle of continuous development, continuous integration, continuous testing, and continuous deployment, aka CI/CD.

Future-Proofing

Consumers are coming to expect a certain level of OTA updates from manufacturers and the demand for this is only set to grow. Vehicle Software Intelligence enables a continuous development cycle because it allows developers in different domains to work at their own pace, without waiting for hardware upgrades to deploy their updates.

It also simplifies the update process. "Unlike existing update technologies that compare binary files," states the whitepaper, "Line-of-Code updates that are based on VSI algorithms can take advantage of the intimate understanding of the vehicle software code." This makes updates less costly to the manufacturer compared to other update methods and enables a far superior user experience with zero-downtime updates.

Beyond this, it also sets the vehicle architecture up for the future. As domain-level software is combined and consolidated into more powerful platforms, segments of code from multiple sources will need to be integrated. Using Vehicle Software Intelligence can speed up this process, even if that code comes from third parties or has been written for different platforms.

Utilizing AI in this way is changing the way manufacturers approach their vehicles and will usher in a new era of software-defined vehicles. To dive deeper into Vehicle Software Intelligence and how it works, take a look at the full whitepaper here.