We propose a novel solution for predicting future trajectories of
pedestrians. Our method uses a multimodal encoder-decoder transformer
architecture, which takes as input both pedestrian locations and ego-vehicle
speeds. Notably, our decoder predicts the entire future trajectory in a
single-pass and does not perform one-step-ahead prediction, which makes the
method effective for embedded edge deployment. We perform detailed experiments
and evaluate our method on two popular datasets, PIE and JAAD. Quantitative
results demonstrate the superiority of our proposed model over the current
state-of-the-art, which consistently achieves the lowest error for 3 time
horizons of 0.5, 1.0 and 1.5 seconds. Moreover, the proposed method is
significantly faster than the state-of-the-art for the two datasets of PIE and
JAAD. Lastly, ablation experiments demonstrate the impact of the key multimodal
configuration of our method.

我们提出了一种新颖的解决方案，用于预测行人的未来轨迹。我们的方法使用多模态编码 - 解码变换器架构，以行人位置和自车速度作为输入。值得注意的是，我们的解码器一次性预测整个未来轨迹，而不进行一步预测，这使得该方法适用于嵌入式边缘部署。我们在两个流行数据集 PIE 和 JAAD 上进行了详细实验和评估，定量结果证明我们所提出的模型在 0.5、1.0 和 1.5 秒三个时间范围内始终具有最低误差，并且相对于现有最先进技术的速度更快。此外，消融实验证明了我们方法的关键多模态配置的影响。