Trajectory prediction has been a long-standing problem in intelligent systems
like autonomous driving and robot navigation. Models trained on large-scale
benchmarks have made significant progress in improving prediction accuracy.
However, the importance on efficiency for real-time applications has been less
emphasized. This paper proposes an attention-based graph model, named GATraj,
which achieves a good balance of prediction accuracy and inference speed. We
use attention mechanisms to model the spatial-temporal dynamics of agents, such
as pedestrians or vehicles, and a graph convolutional network to model their
interactions. Additionally, a Laplacian mixture decoder is implemented to
mitigate mode collapse and generate diverse multimodal predictions for each
agent. GATraj achieves state-of-the-art prediction performance at a much higher
speed when tested on the ETH/UCY datasets for pedestrian trajectories, and good
performance at about 100 Hz inference speed when tested on the nuScenes dataset
for autonomous driving. We conduct extensive experiments to analyze the
probability estimation of the Laplacian mixture decoder and compare it with a
Gaussian mixture decoder for predicting different multimodalities. Furthermore,
comprehensive ablation studies demonstrate the effectiveness of each proposed
module in GATraj. The code is released at
https://github.com/mengmengliu1998/GATraj.