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.

翻译：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运动。尽管当前的运动预测模型精度很高，但其在训练资源需求以及嵌入式硬件部署方面往往带来显著挑战。我们提出了一种新型高效运动预测模型，该模型仅需在单GPU上训练数小时即可获得极具竞争力的基准测试结果。得益于轻量化的架构设计以及对降低训练资源需求的专注，我们的模型能够轻松应用于定制数据集。此外，其低推理延迟特性使其特别适合在计算资源有限的自动驾驶应用中部署。