Anticipating the motion of other road users is crucial for automated driving systems (ADS), as it enables safe and informed downstream decision-making and motion planning. Unfortunately, contemporary learning-based approaches for motion prediction exhibit significant performance degradation as the prediction horizon increases or the observation window decreases. This paper proposes a novel technique for trajectory prediction that combines a data-driven learning-based method with a velocity vector field (VVF) generated from a nature-inspired concept, i.e., fluid flow dynamics. In this work, the vector field is incorporated as an additional input to a convolutional-recurrent deep neural network to help predict the most likely future trajectories given a sequence of bird's eye view scene representations. The performance of the proposed model is compared with state-of-the-art methods on the HighD dataset demonstrating that the VVF inclusion improves the prediction accuracy for both short and long-term (5~sec) time horizons. It is also shown that the accuracy remains consistent with decreasing observation windows which alleviates the requirement of a long history of past observations for accurate trajectory prediction. Source codes are available at: https://github.com/Amir-Samadi/VVF-TP.

翻译：预测其他道路使用者的运动对于自动驾驶系统（ADS）至关重要，因为它能够实现安全且信息充分的下游决策与运动规划。然而，当前基于学习的运动预测方法在预测时间跨度增加或观测窗口减小时，会表现出显著的性能下降。本文提出了一种新颖的轨迹预测技术，它将数据驱动的学习方法与从自然启发概念（即流体动力学）生成的速度矢量场（VVF）相结合。在本工作中，矢量场作为额外输入被引入卷积-递归深度神经网络，以帮助在给定一系列鸟瞰场景表示的情况下预测最可能的未来轨迹。在HighD数据集上，所提模型的性能与最先进方法进行了比较，结果表明加入VVF可同时提高短期和长期（5秒）时间跨度的预测精度。此外，在观测窗口减小时，预测精度仍保持稳定，从而缓解了对长历史观测记录以实现精确轨迹预测的需求。源代码获取地址：https://github.com/Amir-Samadi/VVF-TP。