University of Michigan (U-M) researchers are teaching self-driving cars to recognize and predict pedestrian movements with greater precision than current technologies by zeroing in on humans’ gait, body symmetry and foot placement.
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 and can predict poses and future locations for one or several pedestrians up to about 50 yards from the vehicle.
To create the dataset used to train U-M’s neural network, researchers parked a vehicle with L4 autonomous features at several Ann Arbor intersections. With the car’s cameras and LiDAR facing the intersection, the vehicle could record multiple days of data at a time.