Princeton University researchers have developed a new automated system to help drivers spot hazards hidden around corners.
The new system combines AI and radar to distinguish objects including cars, bicyclists and pedestrians and gauge their direction and oncoming speed.
According to the scientists, the new system utilizes Doppler radar – a type of radar used for tracking speeders and fastballs – to bounce radio waves off surfaces such as buildings and parked automobiles.
When the radar signal hits the surface at an angle, its reflection rebounds off like a cue ball hitting the wall of a pool table, and the signal goes on to strike objects hidden around the corner.
Some of the radar signal bounces back to detectors mounted on the car, allowing the system to see objects around the corner and tell whether they are moving or stationary.
“This will enable cars to see occluded objects that today’s LIDAR and camera sensors cannot record, for example, allowing a self-driving vehicle to see around a dangerous intersection” intones assistant professor Felix Heide.
“The radar sensors are also relatively low-cost, especially compared to lidar sensors, and scale to mass production.”
According to Heide, the algorithms they’ve developed are also highly efficient and fit on current generation automotive hardware systems – implying they could become a mainstay in next-gen vehicles.
To allow the system to distinguish objects, Heide’s team processed part of the radar signal that standard radars consider background noise rather than usable information.
The team then applied AI techniques to refine the processing and read the images.
Fangyin Wei, one of the paper’s lead authors, said the computer running the system had to learn to recognize cyclists and pedestrians from a very sparse amount of data.
“First we have to detect if something is there. If there is something there, is it important? Is it a cyclist or a pedestrian? Then we have to locate it.”
Wei said the system currently detects pedestrians and cyclists because the engineers felt those were the most challenging objects because of their small size and varied shape and motion.
She nevertheless opines that the system can be further adjusted to detect vehicles as well.
Image and content: Princeton University