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:
this https URL

综合数据驱动的学习法和从流体流动动力学概念中生成的速度矢量场，本文提出了一种新的轨迹预测技术，将矢量场作为卷积 - 递归深度神经网络的附加输入，帮助预测基于俯视图场景的未来轨迹。该方法在 HighD 数据集上与最先进的方法进行了比较，展示了在短期和长期（5 秒）时间范围内，速度矢量场的引入提高了预测准确性，并且准确性随着观测窗口的减小而保持一致，缓解了对准确轨迹预测所需的过去观测历史的要求。