Driverless Cars Recognize Peds Better With Deep Learning Algorithm - The New Stack

Autonomous cars use a variety of technologies like radar, lidar, odometry and computer vision to detect objects and people on the road, prompting it to adjust its trajectory accordingly. But these tools can drive up the cost of driverless cars, and still aren't as effective as the human brain in visually distinguishing some objects from pedestrians. To tackle this problem, electrical engineers from University of California, San Diego used powerful machine learning techniques in a recent experiment that incorporated so-called deep learning algorithms in a pedestrian-detection system that performs in near real-time, using visual data only. "We're aiming to build computer vision systems that will help computers better understand the world around them," said Nuno Vasconcelos, an electrical engineering professor at the University of California San Diego who led the study, quoted in a story posted by UC San Diego's Jacobs School of Engineering. The findings, which were presented at the International Conference on Computer Vision in Santiago, Chile, are an improvement over current methods of pedestrian detection, which uses something called cascade detection.