SIMPL: 自動運転のためのシンプルで効率的なマルチエージェント運動予測ベースライン
SIMPL: A Simple and Efficient Multi-agent Motion Prediction Baseline for Autonomous Driving
自動運転向けに、対称的なメッセージパッシングで全交通参加者の将来運動を一度に予測する軽量・高精度な運動予測モデルを提案し、Argoverseベンチマークで競争力のある性能と低遅延を実現した。
著者: Lu Zhang, Peiliang Li, Sikang Liu, Shaojie Shen
分類: cs.RO, cs.AI, cs.CV, cs.LG
原文アブストラクト
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.
