The pandemic has forced self-driving car companies off the road. Now they’re moving forward by refining their mountains of training data.

What’s new: Self-driving cars typically collect real-world training data with two human operators onboard, but Covid-19 makes that unsafe at any speed. Instead, several companies are squeezing more value out of work they’ve already done, according to MIT Technology Review.

What they’re doing: Makers of autonomous vehicles are relabeling old data and fine-tuning simulations.

  • Drivers at the autonomous truck company Embark are sifting through four years of past driving records, flagging noteworthy events and annotating how vehicles should react.
  • Pittsburgh-based Aurora Innovation reassigned vehicle operators to scan its data for unusual situations that can be converted into simulated training scenarios.
  • Scale AI, a data-labeling firm, is adding detail to its datasets. It’s also developing a tool that predicts the intentions of drivers and pedestrians by tracking their gaze.
  • GM’s Cruise is updating its simulations. For instance, the company is improving the way it scores vehicle responses to uncommon occurrences such as encounters with ambulances.

Behind the news: With little income, $1.6 million in average monthly overhead, and increasingly tight funding, autonomous vehicle companies are making tough choices. Lyft, Kodiak Robotics, and Ike have laid off employees, while Zoox is looking for a buyer.

Why it matters: Data can be a renewable resource: By adding new labels and sharpening old ones, AI teams can imbue old datasets with new life. Using refurbished datasets to improve simulations compounds the effect.

We’re thinking: Development of self-driving cars had moved into the slow lane even before the pandemic. It’s better to keep making incremental progress than none at all.

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