3D-mapping technique could improve navigation for autonomous vehicles

Because the technique works effectively with limited computational resources, it holds promise for improving the navigation of autonomous vehicles.

“Most autonomous vehicles use powerful AI programs called vision transformers to take 2D images from multiple cameras and create a representation of the 3D space around the vehicle,” said Tianfu Wu, corresponding author of a paper on the work and an associate professor of electrical and computer engineering at North Carolina State University. “However, while each of these AI programs takes a different approach, there is still substantial room for improvement.

Wu continued: “Our technique, called Multi-View Attentive Contextualization [MvACon], is a plug-and-play supplement that can be used in conjunction with these existing vision transformer AIs to improve their ability to map 3D spaces. The vision transformers aren’t getting any additional data from their cameras, they’re just able to make better use of the data.”

MvACon modifies Patch-to-Cluster attention (PaCa), which Wu and his collaborators released last year. PaCa allows transformer AIs to more efficiently and effectively identify objects in an image.