SIMPL: A Simple and Efficient Multi-Agent Motion Prediction Baseline for Autonomous Driving.
IEEE Robotics Autom. Lett.（2024）
摘要
This paper presents a
S
imple and eff
I
cient
M
otion
P
rediction base
L
ine (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
.
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关键词
Deep Learning Methods,Intelligent Transportation Systems,Representation Learning
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