In this paper, we propose a human trajectory prediction model that combines a Long Short-Term Memory (LSTM) network with an attention mechanism. To do that, we use attention scores to determine which parts of the input data the model should focus on when making predictions. Attention scores are calculated for each input feature, with a higher score indicating the greater significance of that feature in predicting the output. Initially, these scores are determined for the target human position, velocity, and their neighboring individual's positions and velocities. By using attention scores, our model can prioritize the most relevant information in the input data and make more accurate predictions. We extract attention scores from our attention mechanism and integrate them into the trajectory prediction module to predict human future trajectories. To achieve this, we introduce a new neural layer that processes attention scores after extracting them and concatenates them with positional information. We evaluate our approach on the publicly available ETH and UCY datasets and measure its performance using the final displacement error (FDE) and average displacement error (ADE) metrics. We show that our modified algorithm performs better than the Social LSTM in predicting the future trajectory of pedestrians in crowded spaces. Specifically, our model achieves an improvement of 6.2% in ADE and 6.3% in FDE compared to the Social LSTM results in the literature.

翻译：本文提出一种结合长短期记忆网络（LSTM）与注意力机制的人类轨迹预测模型。通过引入注意力分数，模型能够确定在预测过程中需重点关注输入数据的哪些部分。针对每个输入特征计算注意力分数，分数越高表示该特征对输出预测的重要性越大。首先为目标行人的位置、速度及其邻近个体的位置和速度分别计算注意力分数。利用注意力分数，模型可优先处理输入数据中最相关的信息，从而提升预测准确性。我们从注意力机制中提取注意力分数，并将其集成至轨迹预测模块以实现人类未来轨迹预测。为此，引入一个新的神经层，在提取注意力分数后进行加工处理，并与位置信息进行拼接。在公开的ETH与UCY数据集上评估所提方法，采用最终位移误差（FDE）和平均位移误差（ADE）指标衡量模型性能。实验结果表明，在拥挤空间的行人轨迹预测任务中，改进后算法优于Social LSTM。具体而言，与文献中Social LSTM的结果相比，本模型在ADE指标上提升6.2%，在FDE指标上提升6.3%。