A simple pattern of black-and-white stripes could cause an autonomous vehicle or robot to misjudge how far away an obstacle is, potentially triggering an unexpected maneuver or even a collision, according to new University of Florida research.

The finding exposes a previously overlooked vulnerability in the technology that autonomous systems use to perceive depth—a critical capability for vehicles, drones and robots navigating their surroundings. The study will be presented in November at the Association for Computing Machinery Conference on Computer and Communications Security, or ACM CCS and appears on the preprint server arXiv.

Researchers found that repeated patterns in the environment can interfere with the algorithms and artificial intelligence models that estimate the distance between a camera and an object. By manipulating the pattern, an attacker could make an obstacle appear significantly closer or farther away than it really is.

But the problem doesn’t necessarily require an attacker, said Sara Rampazzi, Ph.D., an assistant professor in the Department of Computer and Information Science and Engineering at UF.

“These things can also happen naturally, so it’s not a matter of imagining a sophisticated attacker. It becomes a safety problem,” said Rampazzi, lead of the research effort.

Repeated patterns distort stereo depth

The research focuses on autonomous systems that use stereo cameras—two cameras positioned close together—to estimate depth. These systems can use specific algorithms or artificial intelligence to make the same calculation from images captured by the cameras.

Although the approaches differ, the researchers found they share an underlying weakness. Repeated visual patterns, like stripes on a fence or other regularly spaced objects, can cause the systems to incorrectly calculate depth.

Rampazzi’s team, in collaboration with two universities in Japan, tested multiple sensors, algorithms and AI models and found the same fundamental vulnerability across them, suggesting the problem is not limited to a particular manufacturer or application.

In a driving scenario, for example, researchers projected a simple checkerboard-like pattern onto the back of a vehicle. The pattern caused the autonomous vehicle’s perception system to interpret part of the vehicle as closer than it actually was—close enough to trigger an automatic response such as braking.

The same type of vulnerability could affect drones and ground robots, which rely on depth perception to avoid buildings, vehicles, birds and other obstacles while following an assigned route.

Small patterns, sudden maneuvers

“For a self-driving vehicle, an unexpected hard brake could create a hazard for vehicles following behind it,” Rampazzi said. “For a drone or robot, an incorrect depth estimate could cause a sudden maneuver or collision.”

The researchers also demonstrated that an attacker would not necessarily need to cover an entire scene with a pattern. A small, strategically placed pattern can be enough to influence the depth calculation.

Fixing the underlying depth calculation

Rampazzi said the research team also developed defenses designed to address the underlying cause of the vulnerability rather than simply training AI models with more examples of problematic patterns.

For traditional depth-estimation algorithms, the researchers developed a software-based change that can recognize situations involving repeated patterns and prevent the system from selecting an incorrect depth calculation. For AI-based systems, they modified the models so they can respond differently when those patterns appear.

The researchers tested their approach using simulations as well as a real vehicle and sensors at UF’s controlled autonomous-vehicle testing facility.

Resilience beyond training data

Rampazzi said the work illustrates a broader challenge as AI becomes increasingly responsible for interpreting the physical world.

“We try to find and resolve the root cause of the problem, instead of just adding more data on it, like having a more fancy AI model on it,” she said.

Rather than simply teaching an AI system to recognize every possible problematic pattern, she said, the researchers focus on addressing weaknesses inherited from the sensing and depth-estimation systems underneath the AI to make the technology more resilient to both deliberate attacks and unexpected conditions in the real world.

Who’s behind this story?

Sadie Harley

BSc Life Sciences & Ecology. Microbiology lab background with pharmaceutical news experience in oil, gas, and renewable industries.

Full profile →

Robert Egan

Bachelor’s in mathematical biology, Master’s in creative writing. Well-traveled with unique perspectives on science and language.

Full profile →