Radar outputs of detected objects are sometimes ignored by the vehicle’s software to deal with the generation of “false positives,” said Raj Rajkumar, an electrical and computer engineering professor at Carnegie Mellon University. Without these, the radar would “see” an overpass and report that as an obstacle, causing the vehicle to slam on the brakes.
On the computer vision side of the equation, the algorithms using the camera output need to be trained to detect trucks that are perpendicular to the direction of the vehicle, he added. In most road situations, there are vehicles to the front, back, and to the side, but a perpendicular vehicle is much less common.
“Essentially, the same incident repeats after three years,” Rajkumar said. “This seems to indicate that these two problems have still not been addressed.” Machine learning and artificial intelligence have inherent limitations. If sensors “see” what they have never or seldom seen before, they do not know how to handle those situations. “Tesla is not handling the well-known limitations of AI,” he added.