  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.
