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 this https URL

该研究论文提出了一种针对自动驾驶车辆的简单高效动作预测基准（SIMPL），通过采用紧凑高效的全局特征融合模块和连续轨迹参数化方法，实现对所有相关交通参与者的实时准确运动预测，并在与其他最先进方法的 Argoverse 1 和 2 运动预测基准测试中表现出很高竞争力，同时具有轻量级设计和低推理延迟，适合在实际环境中部署。