The Benefit Of Digital Twin Technology Is In The Sharing
We know what you're probably thinking: Driving simulators and virtual crash testing have been around for decades. So how is this new? A big part of this technology is the ability to create a digital representation of an object or a system once then reuse it in multiple places. Digital Twin Benefits For Manufacturing All cars are made of parts. Many, many parts. Small parts are combined to form bigger components, which ultimately wind up welded, bolted, adhered, or otherwise joined to create a machine you can reasonably buckle yourself into and drive home. When each of those parts is first created as a digital twin, manufacturers can specify the exact shape, weight, and nature of those individual components. Whether they're crafting those components in-house or sourcing them from a supplier, having that data simplifies the process of figuring out how to physically make the thing. Twinning Software Development Each new generation of vehicle contains far more code than those that preceded it. That complexity is magnified by integrating the disparate systems within a car, like the ECU (electronic control unit), driver assistance systems, and infotainment. Reliably testing all that is an integration nightmare. By creating virtual representations of all those disparate systems, software developers can run their solutions within digital twins. That includes the code at the lowest level, basic stuff that controls ignition timing within the engine for example, all the way up to the highest level, like touchscreens responding to user inputs. Autonomy, Too Testing autonomous cars is problematic because it's impossible to safely replicate every condition a driver might encounter on the road. Even if you could, you'd need to do it repeatedly to verify that each new iteration of your autonomous driver didn't break something that formerly worked. Simulation with digital twins makes that feasible. "When we want to test that our autonomy system correctly handles a particular situation, we want to look at a lot of variations of that situation, which allows us to more fully assess our system's capabilities to handle that situation, and not just one particular iteration," Yongjoon Lee said. He's director of simulation at autonomy startup Zoox. Source: Motortrend
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