In a paper published on the preprint server Arxiv.org this week, researchers at Uber’s Advanced Technologies Group (ATG) propose an AI technique to improve autonomous vehicles’ traffic movement predictions. It’s directly applicable to the driverless technologies that Uber itself is developing, which must be able to detect, track, and anticipate surrounding cars’ trajectories in order to safely navigate public roads.
It’s well-understood that without the ability to predict the decisions other drivers on the road might make, vehicles can’t be fully autonomous. In a tragic case in point, an Uber self-driving prototype hit and killed a pedestrian in Tempe, Arizona two years ago, partly because the vehicle failed to detect and avoid the victim. ATG’s research, then — which is novel in that it employs a generative adversarial network (GAN) to make car trajectory predictions as opposed to less complex architectures — promises to advance the state of the art by boosting the precision of predictions by an order of magnitude.