Video: https://slideslive.com/38930749/rasterbased-motion-prediction-for-safe-selfdriving

Abstract: Motion prediction is a critical component of self-driving technology, tasked with inferring future behavior of traffic actors as well as modeling behavior uncertainty. In the talk we focus on this important problem, and discuss raster-based methods that have shown state-of-the-art performance. These approaches take top-down images of a surrounding area as their input, providing near-complete contextual information necessary to accurately predict traffic motion. We present a number of recently proposed models, and show how to develop methods that obey map and other physical constraints of the environment.

Bio: Nemanja Djuric is a Staff Engineer and Tech Lead Manager at Uber ATG, for the past 5 years working on motion prediction, object detection, and other technologies supporting self-driving vehicles. Prior to ATG he worked as a research scientist at Yahoo Labs, which he joined after obtaining his PhD at Temple University.