SIMPL: A Simple and Efficient Multi-agent Motion Prediction Baseline for
  Autonomous Driving
Lu Zhang Peiliang Li Sikang Liu Shaojie Shen
This paper presents a Simple and effIcient Motion Prediction baseLine (SIMPL)
for autonomous vehicles. Unlike conventional agent-centric methods with high
accuracy but repetitive computations and scene-centric methods with compromised
accuracy and generalizability, SIMPL delivers real-time, accurate motion
predictions for all relevant traffic participants. To achieve improvements in
both accuracy and inference speed, we propose a compact and efficient global
feature fusion module that performs directed message passing in a symmetric
manner, enabling the network to forecast future motion for all road users in a
single feed-forward pass and mitigating accuracy loss caused by viewpoint
shifting. Additionally, we investigate the continuous trajectory
parameterization using Bernstein basis polynomials in trajectory decoding,
allowing evaluations of states and their higher-order derivatives at any
desired time point, which is valuable for downstream planning tasks. As a
strong baseline, SIMPL exhibits highly competitive performance on Argoverse 1 &
2 motion forecasting benchmarks compared with other state-of-the-art methods.
Furthermore, its lightweight design and low inference latency make SIMPL highly
extensible and promising for real-world onboard deployment. We open-source the
code at https://github.com/HKUST-Aerial-Robotics/SIMPL.