  Most prior motion prediction endeavors in autonomous driving have
inadequately encoded future scenarios, leading to predictions that may fail to
accurately capture the diverse movements of agents (e.g., vehicles or
pedestrians). To address this, we propose FutureNet, which explicitly
integrates initially predicted trajectories into the future scenario and
further encodes these future contexts to enhance subsequent forecasting.
Additionally, most previous motion forecasting works have focused on predicting
independent futures for each agent. However, safe and smooth autonomous driving
requires accurately predicting the diverse future behaviors of numerous
surrounding agents jointly in complex dynamic environments. Given that all
agents occupy certain potential travel spaces and possess lane driving
priority, we propose Lane Occupancy Field (LOF), a new representation with lane
semantics for motion forecasting in autonomous driving. LOF can simultaneously
capture the joint probability distribution of all road participants' future
spatial-temporal positions. Due to the high compatibility between lane
occupancy field prediction and trajectory prediction, we propose a novel
network with future context encoding for the joint prediction of these two
tasks. Our approach ranks 1st on two large-scale motion forecasting benchmarks:
Argoverse 1 and Argoverse 2.
