Over the last few years, the quest to build fully autonomous vehicles has shifted into high gear. Yet, despite huge advances in both the sensors and artificial intelligence (AI) required to operate these cars, one thing has so far proved elusive: developing algorithms that can accurately and consistently identify objects, movements, and road conditions. As Mathew Monfort, a postdoctoral associate and researcher at the Massachusetts Institute of Technology (MIT) puts it: "An autonomous vehicle must actually function in the real world. However, it's extremely difficult and expensive to drive actual cars around to collect all the data necessary to make the technology completely reliable and safe."
All of this is leading researchers down a different path: the use of game simulations and machine learning to build better algorithms and smarter vehicles. By compressing months or years of driving into minutes or even seconds, it is possible to learn how to better react to the unknown, the unexpected, and unforeseen, whether it is a stop sign obscured by graffiti, a worn or missing lane marking, or snow covering the road and obscuring everything.
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