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Apex.AI Helping Autonomous Vehicles Become Possible

Updated: Oct 21, 2019

| A + T |

Autonomous cars are some of the most complex mobile robots ever developed. And like any other complex robot, there's an enormous amount of work required to get them to the point where they can even begin to solve the problems that you want to solve.

With autonomous driving, the interesting problems are in perception and decision making, but any self-driving platform first must get a car's worth of hardware working with a robotics lab's worth of sensors, and that is difficult to do. Robotics in general has struggled with this problem for decades: It takes a huge amount of time and effort to just get a basic robotic system working reliably, and once you do, there's no guarantee that anything you've come up with will work on hardware that's even slightly different.

About a decade ago, Willow Garage attempted to solve this problem with the Robot Operating System (ROS), an open-source software framework that manages hardware and software integration and communications while also providing libraries, drivers, and software packages for common robot functionality. The goal was to make it so that a researcher could focus on solving a cutting-edge robotics problem without first having to worry about whether their robot would turn on or not, and once that problem was solved, anyone else using ROS could apply that solution to their robot as well. ROS has been very successful, and it's now used in research and development by many companies working on autonomous cars.

Apex.AI is a startup that's taking everything that's great about ROS and adding the reliability, stability, and security that's required to use it as a software framework for commercial autonomous cars. Founded by some of the folks who were involved with ROS from the very beginning, Apex.AI wants to handle all the complicated and boring stuff so that car companies can make progress on everything else.

Much of the robotics development currently done with ROS is intended for research environments, where security is not much of a concern and if your system crashes from time to time, that's okay. ROS can be such a development accelerator that many companies seem to accept these constraints but trying to build this equipment later when you're starting to think about commercial viability is a major headache.

Additionally, ROS itself isn't intended for those sorts of deployments, and is simply missing many of the features that might otherwise be necessary, like verifiable real-time operation. A new version of ROS is currently under development that does include these features, but it's nowhere near ready to be used in a product. This is where Apex.AI comes in, taking the early framework of ROS 2 and building a system on top of it which automotive companies can develop with confidence to be able to deploy it.

Just like ROS, the Apex OS (called Apex.OS) makes it easy to use lots of different types of hardware, as well as different pieces of software if you want to use your own perception or decision-making software. Doing so doesn't jeopardize the integrity of the system as a whole, though Apex.OS makes sure that all the safety-critical tech keeps running so if your in-progress perception stack malfunctions, the vehicle can still brake.

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