Digital Twin Technology Drives the Future of Autonomous Vehicles
Attention upcoming autonomous vehicle (AV) drivers, get ready for the ride of your life as your self-driving car races from zero to 192.2 mph — today’s record speed on a futuristic test track. The stakes are high as the vehicle navigates this thrilling course, decelerating at each twist and turn. That’s because, on this test track, no variable in any given driving scenario is left unturned. Every variable is repeatably and reliably measured.
Although you’re not driving the car, you are driving the greater goal of a test run: to train your car’s autonomous driving algorithm to make the optimal decision every time, with no catastrophic errors.
This sounds like an impossible achievement in the real world, where virtually every factor surrounding a self-driving car is unpredictable, and accidents happen during test runs. However, a new fleet of digital twins is making these idealistic scenarios a reality in today’s digital test environments.
Emulation software and digital twin technology accelerate autonomous vehicle growth
Automakers increasingly enlist emulation software and digital twin technology to accurately test and measure specific automotive components and features in the lab before launching them in the real world — driving autonomous vehicles to market sooner.
By essentially bringing the road to the lab, the automotive industry is undergoing a paradigm shift for innovation — and digital twins are the quiet but powerful forces enabling these new possibilities behind the scenes.
A digital twin represents real-time data and aids in building predictive models that help determine the probability of success of physical prototypes. The global digital twin technology market, at $9.5 billion today, is expected to grow by 22.6% annually, reaching $77.65 billion by 2032, according to a recent report by market research firm Future Market Insights.
The healthcare, telecom, real estate, and retail sectors are all enlisting the help of digital twins. But the largest market share of digital twin technology, at 15%, comes from the transportation and automotive industries.
In the automotive sector, rising applications for simulation, designing, manufacturing, car accident aftermath, and maintenance, repair, and overhaul (MRO) contribute to the need for digital twin technology. As further reported, this technology facilitates everything from testing complex safety scenarios for autonomous vehicles to gauging the importance of maintaining components of a race car engine at risk of getting damaged or burnt out.
Automakers can use digital twin technology to leverage vast quantities of self-driving car test and measurement data to emulate complex scenarios and conditions. Product developers can then delve deeper into how an autonomous vehicle’s artificial intelligence (AI) will respond to unpredictable situations, such as weather conditions like rain, hail, and snow, or other problems like traffic jams.
Digital twins also allow product developers to program a much broader set of functional tests in far less time. For example, researchers can conduct crash-test simulations faster and more safely using digital twin technology.
At a fundamental level, self-driving cars are robots operating in the real world. Digital twins are the digital environments humans can build, manipulate, and control to teach autonomous vehicles how to safely work in the virtual world before integrating their intelligence into real-world scenarios where they interact with spontaneous events and human behaviors.
Digital twin technology and emulation boost autonomous vehicle algorithm training and safety
Today’s automakers are leaning on digital twins for every automotive feature they test, from radar and C-V2X to in-vehicle network and cybersecurity. By integrating multiple digital twins, they can build a comprehensive testing platform to train the car’s autonomous driving algorithm to accurately see and react to complex and dynamic environments.
Keysight’s Radar Scene Emulator, for example, provides complete scene emulation of up to 512 objects at distances as close as 1.5 meters. Enabling radar sensors to see more with a more expansive, continuous field of view bolsters an algorithm’s training to identify and differentiate multiple objects in dense, complex scenes. In turn, automakers can emulate real-world driving scenes in the lab with variations of traffic density, speed, distance, and the total number of targets, moving up testing timelines for common corner cases (unexpected and possibly dangerous situations) while minimizing risks.
Digital twin test and measurement data help overcome complex autonomous vehicle challenges
Autonomous vehicle designers are using digital twins to model sensors, test them against real-world scenarios in the lab, and explore new designs and sensor combinations. In addition, they are being employed to model a car’s in-vehicle network to test network bandwidth and data speed to improve reaction times.
Regarding cybersecurity testing, automakers call on digital twins to test all access points in one design environment to produce a clearer picture of how secure a vehicle is in the real world while removing safety threats.
Unquestionably, using digital twin technology in automotive product development invites some challenges. Emulation, in general, can never wholly represent the real world. Still, by adding noise or stress to a testing situation, the digital twin can come close to replicating real-world scenarios. Likewise, because the variables aren’t entirely random, product developers can control and manipulate them, increasing the prospect of getting it right faster and minimizing costly rework and schedule delays.
What does this mean for the future of automotive innovation? As stated earlier, we’re in for the ride of a lifetime. By bringing the road to the lab, the automotive industry is experiencing a paradigm shift for innovation — and digital twins are the quiet but powerful forces supporting these new and exciting prospects.
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