轻量模型仅需数小时训练，实现高精度轨迹预测。
Efficient Motion Prediction: A Lightweight & Accurate Trajectory Prediction Model With Fast Training and Inference Speed
- 采用轻量化架构设计，降低训练资源需求。
- 单块GPU训练数小时即达领先基准表现。
- 适合边缘设备部署，适用于定制数据集。
为实现高效且安全的自动驾驶，自动驾驶车辆必须能够预测其他交通参与者的行为。尽管现有运动预测模型精度较高，但在训练资源消耗和嵌入式硬件部署方面仍面临重大挑战。本文提出一种新型高效的运动预测模型，仅需在单个GPU上训练数小时即可达到具有竞争力的基准性能。由于采用了轻量化架构并聚焦于减少训练资源需求，该模型可轻松应用于自定义数据集。此外，其低推理延迟特别适合计算资源有限的自动驾驶场景部署。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。