Teaching self-driving cars to predict pedestrian movement

Data collected by vehicles through cameras, LiDAR and GPS allow the researchers to capture video snippets of humans in motion and then recreate them in 3D computer simulation. With that, they've created a "biomechanically inspired recurrent neural network" that catalogs human movements. With it, they can predict poses and future locations for one or several pedestrians up to about 50 yards from the vehicle. "Prior work in this area has typically only looked at still images. It wasn't really concerned with how people move in three dimensions," said Ram Vasudevan, U-M assistant professor of mechanical engineering.