U-M: Teaching self-driving cars to predict pedestrian movement

 By zeroing in on humans’ gait, body symmetry and foot placement, University of Michigan researchers are teaching self-driving cars to recognize and predict pedestrian movements with greater precision than current technologies.

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. That’s at about the scale of a city intersection. Ramanarayan Vasudeva n “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. “But if these vehicles are going to operate and interact in the real world, we need to make sure our predictions of where a pedestrian is going doesn’t coincide with where the vehicle is going next.”

Equipping vehicles with the necessary predictive power requires the network to dive into the minutiae of human movement: the pace of a human’s gait (periodicity), the mirror symmetry of limbs, and the way in which foot placement affects stability during walking.

Much of the machine learning used to bring autonomous technology to its current level has dealt with two dimensional images—still photos. A computer shown several million photos of a stop sign will eventually come to recognize stop signs in the real world and in real time.

But by utilizing video clips that run for several seconds, the U-M system can study the first half of the snippet to make its predictions, and then […]

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