While autonomous driving has long relied on machine learning to plan routes and detect objects, some companies and researchers are now betting that generative AI — models that take in data of their surroundings and generate predictions — will help bring autonomy to the next stage. Wayve, a Waabi competitor, released a comparable model last year that is trained on the video that its vehicles collect. 

Waabi’s model works in a similar way to image or video generators like OpenAI’s DALL-E and Sora. It takes point clouds of lidar data, which visualize a 3D map of the car’s surroundings, and breaks them into chunks, similar to how image generators break photos into pixels. Based on its training data, Copilot4D then predicts how all points of lidar data will move. Doing this continuously allows it to generate predictions 5-10 seconds into the future.

Waabi is one of a handful of autonomous driving companies, including competitors Wayve and Ghost, that describe their approach as “AI-first.” To Urtasun, that means designing a system that learns from data, rather than one that must be taught reactions to specific situations. The cohort is betting their methods might require fewer hours of road-testing self-driving cars, a charged topic following an October 2023 accident where a Cruise robotaxi dragged a pedestrian in San Francisco. 

Waabi is different from its competitors in building a generative model for lidar, rather than cameras. 

“If you want to be a Level 4 player, lidar is a must,” says Urtasun, referring to the automation level where the car does not require the attention of a human to drive safely. Cameras do a good job of showing what the car is seeing, but they’re not as adept at measuring distances or understanding the geometry of the car’s surroundings, she says.

Though Waabi’s model can generate videos showing what a car will see through its lidar sensors, those videos will not be used as training in the company’s driving simulator that it uses to build and test its driving model. That’s to ensure any hallucinations arising from Copilot4D do not get taught in the simulator.

The underlying technology is not new, says Bernard Adam Lange, a PhD student at Stanford who has built and researched similar models, but it’s the first time he’s seen a generative lidar model leave the confines of a research lab and be scaled up for commercial use. A model like this would generally help make the “brain” of any autonomous vehicle able to reason more quickly and accurately, he says.

“It is the scale that is transformative,” he says. “The hope is that these models can be utilized in downstream tasks” like detecting objects and predicting where people or things might move next.

Methane emissions are responsible for nearly a third of the total warming the planet has experienced so far. While there are natural sources of the greenhouse gas, including wetlands, human activities like agriculture and fossil-fuel production have dumped millions of metric tons of additional methane into the atmosphere. The concentration of methane has more than doubled over the past 200 years. But there are still large uncertainties about where, exactly, emissions are coming from.

Answering these questions is a challenging but crucial first step to cutting emissions and addressing climate change. To do so, researchers are using tools ranging from satellites like the recently launched MethaneSAT to ground and aerial surveys. 

The US Environmental Protection Agency estimates that roughly 1% of oil and gas produced winds up leaking into the atmosphere as methane pollution. But survey after survey has suggested that the official numbers underestimate the true extent of the methane problem.  

For the sites examined in the new study, “methane emissions appear to be higher than government estimates, on average,” says Evan Sherwin, a research scientist at Lawrence Berkeley National Laboratory, who conducted the analysis as a postdoctoral fellow at Stanford University.  

The data Sherwin used comes from one of the largest surveys of US fossil-fuel production sites to date. Starting in 2018, Kairos Aerospace and the Carbon Mapper Project mapped six major oil- and gas-producing regions, which together account for about 50% of onshore oil production and about 30% of gas production. Planes flying overhead gathered nearly 1 million measurements of well sites using spectrometers, which can detect methane using specific wavelengths of light. 

Here’s where things get complicated. Methane sources in oil and gas production come in all shapes and sizes. Some small wells slowly leak the gas at a rate of roughly one kilogram of methane an hour. Other sources are significantly bigger, emitting hundreds or even thousands of kilograms per hour, but these leaks may last for only a short period.

The planes used in these surveys detect mostly the largest leaks, above roughly 100 kilograms per hour (though they catch smaller ones sometimes, down to around one-tenth that size, Sherwin says). Combining measurements of these large leak sites with modeling to estimate smaller sources, researchers estimated that the larger leaks account for an outsize proportion of emissions. In many cases, around 1% of well sites can make up over half the total methane emissions, Sherwin says.

But some scientists say that this and other studies are still limited by the measurement tools available. “This is an indication of the current technology limits,” says Ritesh Gautam, a lead senior scientist at the Environmental Defense Fund.

June 2023

Astronomy should, in principle, be a welcoming field for blind researchers. But across the board, science is full of charts, graphs, databases, and images that are designed to be seen.

So researcher Sarah Kane, who is legally blind, was thrilled three years ago when she encountered a technology known as sonification, designed to transform information into sound. Since then she’s been working with a project called Astronify, which presents astronomical information in audio form. 

For millions of blind and visually impaired people, sonification could be transformative—opening access to education, to once unimaginable careers, and even to the secrets of the universe. Read the full story.

—Corey S. Powell

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)

+ It’s time to get into metal detecting (no really, it is!)
+ Meanwhile, over on Mars…
+ A couple in the UK decided to get married on a moving train, because why not?
+ Even giant manta rays need a little TLC every now and again.