Future-proof driverless cars: From proof-of-concept to reach

The automotive industry has gone through a long period of time. The technology to build self-driving cars and autonomous vehicles is no longer science fiction. I’ll admit, it’s really exciting to think of a future world full of space-age vehicles gracefully traversing the planet under careful, precise, coordinated scheduling. The only thing I don’t want to happen is that people in the future are all required to wear the same shiny silver jumpsuits as they do in the movie. I don’t know who came up with that idea?

Of course, the reality is much more complex, and this certainly applies to the technological environment of future driverless cars – especially in the proof-of-concept stage. In addition to a unique and demanding development environment, you’re juggling a wide variety of customized on-premises and cloud applications, all of which need to be able to communicate with each other seamlessly. This is indeed an Industrial Internet of Things (IIoT) system that requires a high degree of autonomy to turn the concept into a living reality. RTI can help you integrate all of this into a reliable, functioning whole that makes your engineering projects run more efficiently.

Gentlemen, start the engine!

As I mentioned in my previous blog, I have been with RTI for almost four years. In the meantime, we have witnessed more and more manufacturers jumping into the torrent of driverless car development. My role is to work with our sales team, partners and strategic customers to help them all succeed because this is an area where RTI can provide a lot.

But when is the right time to ask us at RTI for help? As you pass the proof-of-concept stage, you must avoid the roadblocks that pop up ahead. First, driverless car systems must be able to do three main things: perceive the environment, process data about that environment, and act on the environment based on the information obtained. This is essentially a round-robin cycle, however, the amount of data being generated and the speed at which it needs to be processed can quickly become overwhelming.

Common Challenges of Autonomous Vehicle Systems

Taking a closer look, when we look at a driverless car, it must have a sensor package that can observe the environment. This sensor package can be simple driver-assisted technology, or more complex, high or full. automatic vehicle. It will determine the level of accuracy and quantity of data you collect from lidar sensors, radar sensors, actuators, and other input points. We call it sensor fusion or data fusion because it only really works if all these components are able to share data with each other and agree on the accuracy of the conclusions.

The next thing to consider is where the system has to use AI to solve problems like: “Okay, what do I do with this information? Do I turn left? Do I go straight? Do I turn right? How is the environment?” Analyze different transient factors, such as people, bicycles or cars, and then make decisions and plans. Of course, the car takes some physical action, which in turn changes the environment, so the cycle starts all over again.

So the real challenge is a high level of interconnectivity: your system is only as good as the speed and quality of capturing and processing data. Then when you add things like connecting to the cloud or connecting to other systems, you add external interconnect, which is also part of the interconnectivity solution. So this is a very complex distributed system with a lot of components, all in a very compact package. But by what exactly is it bound together? It needs to be built on a flexible, massively scalable IIoT framework to keep pace with competitors, industry standards and many other variables.

Where RTI’s Comes in: Connext DDS and Hierarchical Data Bus Concepts

Supporting massive scaling is a core premise of every highly autonomous system. This truth applies especially to the self-driving car space, because building a truly market-ready system in a limited test environment is complex enough to make the best development teams lose sight of one another. Entering the market and meeting public demands in all media scrutiny and new testing scenarios often requires adding a whole new layer of mission-critical to the system, which no one has been able to do so far.

As I always say, when you get to that stage – getting a system working reliably and into production – that’s where we can help. Because RTI can provide a very solid foundation on which you can build your own software. We have been deeply involved in the field of autonomous systems for many years. We’ve been working on autonomous systems for the military long before autonomous vehicles became the buzzword in the auto industry. Trying to do all the work yourself is likely to be ineffective, especially if you could have used RTI’s expertise to tackle difficult challenges, such as software infrastructure and communications.

Our Connext DDS software is a good example of this capability, as it uses a layered data bus to manage communications. Hierarchical data bus is a concept and term proposed by the Industrial Internet Consortium (IIC), of which RTI is a member. We wrote some documentation and specifications for the IIC. One of the results of working with other companies is the creation of a layered data bus that allows you to differentiate between the flow of control and information at different levels in your system. In addition to having global control, it also allows you to arbitrarily set “quality of service” to determine how data flows between applications in different scenarios, including reliability, bandwidth and latency.

This concept of a layered data bus allows us to use the same standard across the ecosystem. We can customize different data management conditions and rules for different parts of the system. This allows us to communicate between different systems in a very standardized way without having to add new protocols and gateways or other bridges. As part of Connext DDS, a hierarchical data bus makes it easy to find these different data usage conditions, making data management reliable and repeatable.

In the end, what we want is to free up your development team to focus on building the cars of the future. But when it comes to the interconnectivity framework, why reinvent the wheel? Check out this data sheet to learn more about Connext DDS and how it’s doing in the automotive industry.

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