Leveraging Future Relationship Reasoning for Vehicle Trajectory
  Prediction
Daehee Park Hobin Ryu Yunseo Yang Jegyeong Cho Jiwon Kim Kuk-Jin Yoon
Understanding the interaction between multiple agents is crucial for
realistic vehicle trajectory prediction. Existing methods have attempted to
infer the interaction from the observed past trajectories of agents using
pooling, attention, or graph-based methods, which rely on a deterministic
approach. However, these methods can fail under complex road structures, as
they cannot predict various interactions that may occur in the future. In this
paper, we propose a novel approach that uses lane information to predict a
stochastic future relationship among agents. To obtain a coarse future motion
of agents, our method first predicts the probability of lane-level waypoint
occupancy of vehicles. We then utilize the temporal probability of passing
adjacent lanes for each agent pair, assuming that agents passing adjacent lanes
will highly interact. We also model the interaction using a probabilistic
distribution, which allows for multiple possible future interactions. The
distribution is learned from the posterior distribution of interaction obtained
from ground truth future trajectories. We validate our method on popular
trajectory prediction datasets: nuScenes and Argoverse. The results show that
the proposed method brings remarkable performance gain in prediction accuracy,
and achieves state-of-the-art performance in long-term prediction benchmark
dataset.