  One of the critical pieces of the self-driving puzzle is understanding the
surroundings of a self-driving vehicle (SDV) and predicting how these
surroundings will change in the near future. To address this task we propose
MultiXNet, an end-to-end approach for detection and motion prediction based
directly on lidar sensor data. This approach builds on prior work by handling
multiple classes of traffic actors, adding a jointly trained second-stage
trajectory refinement step, and producing a multimodal probability distribution
over future actor motion that includes both multiple discrete traffic behaviors
and calibrated continuous position uncertainties. The method was evaluated on
large-scale, real-world data collected by a fleet of SDVs in several cities,
with the results indicating that it outperforms existing state-of-the-art
approaches.
