Why the advent of autonomous vehicles will create new challenges for IT teams in the automotive industry.
From built-in GPS systems to fully autonomous vehicles, IoT has infiltrated the automotive world over the past few years. We’re even seeing automakers delve into distributed-ledgers (i.e., blockchain), with Volkswagen’s recent announcement around their use of the IOTA “Tangle” to deliver software updates to autonomous cars as early as Spring 2019.
These technological advancements have raised discussions around everything from legislation to programming ethics, but one lacking area of conversation is that of how this new technology will impact the IT teams charged with progressing the future of transportation.
Ingesting and analyzing the vast amount of data generated by connected cars will continue to be a major challenge for the IT department.
The New Rules of the Road
As self-driving vehicles take to the roads, the market for engineers who can build and maintain simulated environments for vehicle testing, create new behavior prediction models, and improve computer vision algorithms has exploded. Just look at Uber and Waymo (Google) job boards for examples.
Undoubtedly, these automotive innovations will create a need for next-generation operations teams that can deal with a massive influx of real-time data and all that it entails to keep our roads running smoothly — and safely. That’s why there’s already a rise in the number of IT positions related specifically to the testing and deployment of automated driving systems and connected vehicles.
Though fully independent vehicles are still a ways off for the general consumer market, IT professionals in the automotive industry can ready themselves by reviewing the SAE International global standards around which all automated driving systems are based. These standards define the difference between the various levels of automation involved, ranging from accident avoidance systems through to fully autonomous driving with no driver supervision. It’s safe to assume that the SAE standards will become translated into operational protocols and SLAs for the IT teams that will find themselves, for example, supporting teams of vehicle dispatchers for “platooned” commercial vehicles or remote drivers.
Supporting the Future Through a Common Language
At the heart of all of connected cars is often an ecosystem of software called Robot OS (ROS). Beyond automotives, this open-source project powers many of today’s autonomous IoT devices. Needless to say, it will be important for IT, especially DevOps teams, to develop more than a passing knowledge of ROS to provide proper support.
But even being familiar with ROS may just be scratching the surface; there’s already a whole slew of other software that goes into the construction, testing, and operation of autonomous vehicles. Some of the languages and technologies you may see in use include:
- Robot OS
- NoSQL (NoSQL has improved performance in low-latency environments vs SQL)
- Apache Kafka
- Apache Hadoop
- Edge Computing
While most autonomous vehicle systems are rooted in common technologies and programming languages in use today, every vehicle manufacturer uses a slightly different combination, so the learning curve can get steep. All things considered, strong C++ is probably the most important basic skill for IT and DevOps teams that will be dealing directly with the programming and operation of self-driving vehicles.
More Connections Mean More Complexity
Ingesting and analyzing the vast amount of data generated by connected cars will continue to be a major challenge for the IT department. Applying artificial intelligence and machine learning in the cloud will be a crucial step for the overall success of future auto innovations, especially when it comes to heavily automated vehicles. As these vehicles are expected to make in-the-moment decisions with limited or no human intervention, latency issues will be the tallest mountain to climb before we can deem the vehicles safe.
As such, edge computing has quickly become a necessity for automated driving systems. For example, by applying AI and ML to the “edge” of the internet by bringing a large part of it in-vehicle (and in-highway), latency issues can be avoided — or at least mitigated. As these intelligent vehicles absorb mass amounts of data through sensors, edge computing gives us the ability to reduce this data into only essential information, limiting what is being sent back and forth from the cloud and vehicle during operation. But new technology brings the need for oversight — and as AIOps systems support the future of transportation, IT experts will be tasked with the important role of monitoring and optimizing these systems.
The vision for autonomous cars that fly through the sky may seem like a distant dream, but it’s important to recognize that a new future of intelligent, connected vehicles is probably closer to reality than you might expect.
About the author
Dominic Wellington is the Director of Strategic Architecture at Moogsoft. He has been involved in IT operations for a number of years, working in fields as diverse as SecOps, cloud computing, and data center automation.