Driving autonomy into the metaverse
Researchers at Carnegie Mellon have proposed the possibility of generating safety-critical autonomous vehicle scenarios in the metaverse to understand them by way of digital twins. Because car crashes are rare and heavily dependent on diverse environments like individual driving habits and road conditions, gathering data in the real world on safety-critical scenarios is nearly impossible, say the researchers.
Instead, the researchers are proposing the idea of gathering that data in the metaverse to generate diverse safety-critical scenarios that can quickly adapt to various environments to provide numerous realistic testing cases, without the need to track hundreds of miles.
“Autonomous driving has demonstrated promising potential to reduce crashes, save people’s time, and combat climate change,” says Ding Zhao, an assistant professor of mechanical engineering. “But it’s evident that the guarantee of safety is still missing. We want to develop that missing piece for the large-scale deployment of self-driving.”
One way to generate safety-critical autonomous system scenarios is by finding failure cases by trial and error. Digital twins provide perfect test beds to create safety-critical scenarios without causing damage in the real-world.
The researchers proposed a new method, – Learning to Collide – to identify risky scenarios leveraging the reinforcement learning technique. This method builds a framework where the autonomous system is a victim attacked by the scenario-generation algorithm.
Another efficient way to generate desired scenarios in digital twins is using causality, which describes the cause-and-effect relationships between objects. For example, an accident between a pedestrian and a vehicle is caused by another vehicle blocking the view of the pedestrian.
The researchers developed a method called Causal Autoregressive Flow (CausalAF) to generate safety-critical scenarios in autonomous digital twins. It uses causality summarized by human experts and enables efficient generation to find diverse risky scenarios to self-driving vehicles.
“What we are doing is unique because typically the cause of inference is studied by statisticians for theory, but we are applying it to the real-world,” says Wenhao Ding, a student in Zhao’s lab. “We are building the bridge between the digital world and the real world, and we believe that it is the most efficient way to ensure the safety of people using digital systems.”
Understanding these scenarios extends beyond the road, as the use of autonomous machines is actively explored in manufacturing. The researchers have created a digital twin of Mill 19, the home of Carnegie Mellon’s Manufacturing Futures Initiative to more efficiently and cost-effectively explore safety in manufacturing.
“Manufacturing scenarios have human-robot interactions, and through a digital twin we can assess working conditions.” says Zhao. “Scenarios with safety and security problems may be rare, but they do happen. It is critical for us to understand the why in order to prevent recurrences.”
For more, see “A Survey on Safety-Critical Driving Scenario Generation – A Methodological Perspective.”
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