Predicting the future trajectories of multiple pedestrians in certain scenes is critical for autonomous moving platforms (like, self-driving cars and social robots). In this paper, we propose a novel Generative Adversarial Network model with Transformers, which simulates the pedestrian distribution to capture the uncertainty of the predicted paths and generate more reasonable future trajectories. The design of our method includes a generator and a discriminator. The generator mainly contains an encoder, a decoder, and a prediction module. Specifically, the encoder and the decoder comprise multihead convolutional self-attention to learn the sequence of historical movement, and the prediction module incorporates the Mish Feed-Forward Network to yield the predicted target. The discriminator takes both the predicted paths and ground truth as input, classifies them as socially acceptable or not. Experimental results show that the proposed method consistently boosts the performance of trajectory forecasting, and our framework surpasses several existing baselines by evaluating the results on various data sets. Code is available at https://github.com/lzz970818/Trajectory-Prediction.

中文翻译：

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用于行人轨迹预测模型的带有变压器的改进 GAN

在某些场景中预测多个行人的未来轨迹对于自动移动平台（如自动驾驶汽车和社交机器人）至关重要。在本文中，我们提出了一种新颖的带有 Transformers 的生成对抗网络模型，它模拟行人分布以捕捉预测路径的不确定性并生成更合理的未来轨迹。我们方法的设计包括一个生成器和一个鉴别器。生成器主要包含编码器、解码器和预测模块。具体来说，编码器和解码器包括多头卷积自注意力来学习历史运动的序列，而预测模块结合了 Mish 前馈网络来产生预测目标。鉴别器将预测路径和地面实况作为输入，将它们归类为社会可接受与否。实验结果表明，所提出的方法不断提高轨迹预测的性能，并且我们的框架通过评估各种数据集的结果超过了几个现有的基线。代码可在 https://github.com/lzz970818/Trajectory-Prediction 获得。