A new radar system, easily integrated into today’s vehicles, uses Doppler radar to bounce radio waves off surfaces such as buildings and parked automobiles. The radar signal hits the surface at an angle, so its reflection rebounds off like a cue ball hitting the wall of a pool table. 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.
The system 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. The radar sensors are also relatively low-cost, especially compared to LiDAR sensors, and scale to mass production. The system is able to distinguish objects including cars, bicyclists, and pedestrians and gauge their direction and oncoming speed.
In recent years, engineers have developed a variety of sensor systems that allow cars to detect other objects on the road. Many of them rely on LiDAR or cameras using visible or near-infrared light; such sensors preventing collisions are now common on modern cars. But optical sensing is difficult to use to spot items out of the car’s line of sight. In earlier research, the team used light to see objects hidden around corners. But those efforts currently are not practical for use in cars both because they require high-powered lasers and are restricted to short ranges.
In conducting that earlier research, the team investigated the possibility of creating a system to detect hazards out of the car’s line of sight using imaging radar instead of visible light. The signal loss at smooth surfaces is much lower for radar systems and radar is a proven technology for tracking objects. The challenge is that radar’s spatial resolution — used for picturing objects around corners such as cars and bikes — is relatively low. The researchers believed that they could create algorithms to interpret the radar data to allow the sensors to function. The algorithms are highly efficient and fit on current-generation automotive hardware systems.
To allow the system to distinguish objects, the team processed part of the radar signal that standard radars consider background noise rather than usable information. The team applied artificial intelligence techniques to refine the processing and read the images. The computer running the system had to learn to recognize cyclists and pedestrians from a very sparse amount of data.
The system currently detects pedestrians and cyclists because the engineers felt those were the most challenging objects due to their small size and varied shape and motion. The system could be adjusted to detect vehicles as well.
The researchers plan to follow the research in a number of directions for applications involving both radar and refinements in signal processing. The system has the potential to radically improve automotive safety. Since it relies on existing radar sensor technology, readying the radar system for deployment in the next generation of automobiles should be possible.