The dark climate secret of self-driving cars

In many visions of the future, self-driving electric cars will whoosh through our cities, picking up passengers - but is there a hidden environmental cost to the technology?

New research from MIT suggests that the energy required to run the computers in a global fleet of autonomous cars could generate as many greenhouse gas emissions as all the data centres in the world today.

The research highlighted the sheer amount of computing required to keep billions of self-driving vehicles on the road - with up to 21.6 quadrillion calculations per day (one quadrillion is 1,000 trillion).

Data centres account for about 0.3 percent of global greenhouse gas emissions, or about as much carbon as the country of Argentina produces annually, according to the International Energy Agency.

MIT researchers built a statistical model to study the problem.

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They determined that 1 billion autonomous vehicles, each driving for one hour per day with a computer consuming 840 watts, would consume enough energy to generate about the same amount of emissions as all the data centres currently on Earth.

The researchers warn that to minimise the impact may require more efficient computers - perhaps requiring faster upgrades than the current rate of technology evolution.

Lead author Soumya Sudhakar, a graduate student in aeronautics and astronautics, says, "If we just keep the business-as-usual trends in decarbonisation and the current rate of hardware efficiency improvements, it doesn't seem like it is going to be enough to constrain the emissions from computing onboard autonomous vehicles.

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“This has the potential to become an enormous problem. But if we get ahead of it, we could design more efficient autonomous vehicles that have a smaller carbon footprint from the start.”

The researchers built a framework to explore the operational emissions from computers on board a global fleet of electric vehicles that are fully autonomous, meaning they don't require a back-up human driver.

The model is a function of the number of vehicles in the global fleet, the power of each computer on each vehicle, the hours driven by each vehicle, and the carbon intensity of the electricity powering each computer.

"On its own, that looks like a deceptively simple equation. But each of those variables contains a lot of uncertainty because we are considering an emerging application that is not here yet," Sudhakar says.

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When they used the probabilistic model to explore different scenarios, Sudhakar was surprised by how quickly the algorithms' workload added up.

For example, if an autonomous vehicle has 10 deep neural networks processing images from 10 cameras, and that vehicle drives for one hour a day, it will make 21.6 million inferences each day.

One billion vehicles would make 21.6 quadrillion inferences.

To put that into perspective, all of Facebook's data centres worldwide make a few trillion inferences each day.

Sertac Karaman, associate professor of aeronautics and astronautics says."After seeing the results, this makes a lot of sense, but it is not something that is on a lot of people's radar. These vehicles could actually be using a ton of computer power. They have a 360-degree view of the world, so while we have two eyes, they may have 20 eyes, looking all over the place and trying to understand all the things that are happening at the same time,

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