There’s one massive thing holding autonomous cars back, and it’s an issue with AI in general, not only self-driving vehicles. Our expert weighs in.
The automobile industry is currently moving in the direction of higher autonomy levels, with the ultimate goal of letting a car move around on its own without requiring any human intervention. We’re not quite there yet, even though the likes of Elon Musk have claimed that the existing advanced driver assistance system on Tesla cars was “probably” better than humans all the way back in 2016. Regulatory bodies have been on the fence about greenlighting ambitious self-driving tech, not because of scientific skepticism but due to a track record of accidents that have resulted in several people losing their lives and instances of self-driving vehicles blocking emergency responders.
The solution, however, could be to reimagine how we train these self-driving systems. “Manufacturers are gathering data on how an autonomous system should be driving from millions of people on the road, but is that really the most authoritative source?” asks Dr. Laine Mears, Automotive Manufacturing Chair at Clemson University’s International Center for Automotive Research. The concerns are valid. Take, for example, Tesla, which relies on audio-visual data from billions of miles driven by millions of Tesla cars currently on the road.
Then there is Shadow Mode, which Tesla describes as “a dormant logging-only mode which lets us validate the performance of the feature in the background based on millions of miles of real-world driving.” But that doesn’t cover edge scenarios, or to put it more specifically, low-frequency, unprecedented rare events, one where human decision-making could be the difference between life and death.
