効率的な運動予測：高速学習と推論を実現する軽量で高精度な軌道予測モデル
Efficient Motion Prediction: A Lightweight & Accurate Trajectory Prediction Model With Fast Training and Inference Speed
自動運転向けに、単一GPUで数時間の学習で高精度を達成し、推論も高速な軽量軌道予測モデルを提案した。
著者: Alexander Prutsch, Horst Bischof, Horst Possegger
分類: cs.RO, cs.CV
原文アブストラクト
For efficient and safe autonomous driving, it is essential that autonomous vehicles can predict the motion of other traffic agents. While highly accurate, current motion prediction models often impose significant challenges in terms of training resource requirements and deployment on embedded hardware. We propose a new efficient motion prediction model, which achieves highly competitive benchmark results while training only a few hours on a single GPU. Due to our lightweight architectural choices and the focus on reducing the required training resources, our model can easily be applied to custom datasets. Furthermore, its low inference latency makes it particularly suitable for deployment in autonomous applications with limited computing resources.
