You’re driving in a winter storm at midnight. Icy rain smashes your windshield, immediately turning it into a sheet of frost. Your eyes dart across the highway, seeking any movement that could be wildlife, struggling vehicles, or highway responders trying to pass. Whether you find safe passage or meet catastrophe hinges on how fast you see and react.

Even experienced drivers struggle with bad weather. For self-driving cars, drones, and other robots, a snowstorm could cause mayhem. The best computer-vision algorithms can handle some scenarios, but even running on advanced computer chips, their reaction times are roughly four times greater than a human’s.

“Such delays are unacceptable for time-sensitive applications…where a one-second delay at highway speeds can reduce the safety margin by up to 27m [88.6 feet], significantly increasing safety risks,” Shuo Gao at Beihang University and colleagues wrote in a recent paper describing a new superfast computer vision system.

Instead of working on the software, the team turned to hardware. Inspired by the way human eyes process movement, they developed an electronic replica that rapidly detects and isolates motion.

The machine eye’s artificial synapses connect transistors into networks that detect changes in the brightness of an image. Like biological neural circuits, these connections store a brief memory of the past before processing new inputs. Comparing the two allows them to track motion.

Combined with a popular vision algorithm, the system quickly separates moving objects, like walking pedestrians, from static objects, like buildings. By limiting its attention to motion, the machine eye needs far less time and energy to assess and respond to complex environments.

When tested on autonomous vehicles, drones, and robotic arms, the system sped up processing times by roughly 400 percent and, in most cases, surpassed the speed of human perception without sacrificing accuracy.

“These advancements empower robots with ultrafast and accurate perceptual capabilities, enabling them to handle complex and dynamic tasks more efficiently than ever before,” wrote the team.

Two Motion Pictures

A mere flicker in the corner of an eye captures our attention. We’ve evolved to be especially sensitive to movement. This perceptual superpower begins in the retina. The thin layer of light-sensitive tissue at the back of the eye is packed with cells fine-tuned to detect motion.

Retinal cells are a curious bunch. They store memories of previous scenes and spark with activity when something in our visual field shifts. The process is a bit like an old-school film reel: Rapid transitions between still frames lead to the perception of movement.

Every cell is tuned to detect visual changes in a particular direction—for example, left to right or up to down—but is otherwise dormant. These activity patterns form a two-dimensional neural map that the brain interprets as speed and direction within a fraction of a second.

“Biological vision excels at processing large volumes of visual information” by focusing only on motion, wrote the team. When driving across an intersection, our eyes intuitively zero in on pedestrians, cyclists, and other moving objects.

Computer vision takes a more mathematical approach.

A popular type called optical flow analyzes differences between pixels across visual frames. The algorithm segments pixels into objects and infers movement based on changes in brightness. This approach assumes that objects maintain brightness as they move. A white dot, for example, remains a white dot as it drifts to the right, at least in simulations. Pixels near each other should also move in tandem as a marker for motion.

Although inspired by biological vision, optical flow struggles in real-world scenarios. It’s an energy hog and can be laggy. Add in unexpected noise—like a snowstorm—and robots running optical flow algorithms will have trouble adapting to our messy world.

Two-Step Solution

To get around these problems, Gao and colleagues built a neuron-inspired chip that dynamically detects regions of motion and then focuses an optical flow algorithm on only those areas.

Their initial design immediately hit a roadblock. Traditional computer chips can’t adjust their wiring. So the team fabricated a neuromorphic chip that, true to its name, computes and stores information at the same spot, much like a neuron processes data and retains memory.

Because neuromorphic chips don’t shuttle data from memory to processors, they’re far faster and more energy-efficient than classical chips. They outshine standard chips in a variety of tasks, such as sensing touch, detecting auditory patterns, and processing vision.

“The on-device adaptation capability of synaptic devices makes human-like ultrafast visual processing possible,” wrote the team.

The new chip is built from materials and designs commonly used in other neuromorphic chips. Similar to the retina, the array’s artificial synapses encode differences in brightness and remember these changes by adjusting their responses to subsequent electrical signals.

When processing an image, the chip converts the data into voltage changes, which only activate a handful of synaptic transistors; the others stay quiet. This means the chip can filter out irrelevant visual data and focus optical flow algorithms on regions with motion only.

In tests, the two-step setup boosted processing speed. When analyzing a movie of a pedestrian about to dash across a road, the chip detected their subtle body position and predicted what direction they’d run in roughly 100 microseconds—faster than a human. Compared to conventional computer vision, the machine eye roughly doubled the ability of self-driving cars to detect hazards in a simulation. It also improved the accuracy of robotic arms by over 740 percent thanks to better and faster tracking.

The system is compatible with computer vision algorithms beyond optical flow, such as the YOLO neural network that detects objects in a scene, making it adjustable for different uses.

“We do not completely overthrow the existing camera system; instead, by using hardware plug-ins, we enable existing computer vision algorithms to run four times faster than before, which holds greater practical value for engineering applications,” Gao told the South China Morning Post.