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。

多模态变压器下的上下文感知行人轨迹预测

Context-aware Pedestrian Trajectory Prediction with Multimodal  Transformer

Crowd behavior understanding is crucial yet challenging across a wide range
of applications, since crowd behavior is inherently determined by a sequential
decision-making process based on various factors, such as the pedestrians' own
destinations, interaction with nearby pedestrians and anticipation of upcoming
events. In this paper, we propose a novel framework of Social-Aware Generative
Adversarial Imitation Learning (SA-GAIL) to mimic the underlying
decision-making process of pedestrians in crowds. Specifically, we infer the
latent factors of human decision-making process in an unsupervised manner by
extending the Generative Adversarial Imitation Learning framework to anticipate
future paths of pedestrians. Different factors of human decision making are
disentangled with mutual information maximization, with the process modeled by
collision avoidance regularization and Social-Aware LSTMs. Experimental results
demonstrate the potential of our framework in disentangling the latent
decision-making factors of pedestrians and stronger abilities in predicting
future trajectories.

本篇论文提出了一种基于社会感知的生成对抗模仿学习结合动态规划的方法，用于模拟人群中的行为方式，并成功地将人类决策因素拆分，更准确地预测未来的轨迹。