Pedestrian trajectory prediction using BiRNN encoder-decoder framework
Pedestrian trajectory prediction using BiRNN encoder-decoder framework
复制标题
使用 BiRNN 编码器-解码器框架进行行人轨迹预测
DOI：
10.1080/01691864.2019.1635910
复制
发表时间：
2019
影响因子：
2
通讯作者：
Asama Hajime
中科院分区：
文献类型：
--
作者：
Wu Jiaxu;Woo Hanwool;Tamura Yusuke;Moro Alessandro;Massaroli Stefano;Yamashita Atsushi;Asama Hajime
在人群中导航的自主移动的机器人需要预见周围行人的未来轨迹，并相应地规划安全路径以避免任何可能的碰撞。本文提出了一种新的行人轨迹预测方法。特别是，我们开发了一种基于双向递归神经网络（BiRNN）的编码器-解码器框架的新方法。由于注意力机制增强了BiRNN的特殊结构，将社会互动纳入模型的困难得到了解决，这是一个独立于每个行人相对重要性的邻近模型。我们和以前的方法之间的主要区别是，BiRNN允许我们使用行人未来状态的信息。我们在几个公共数据集上测试了我们的方法的性能。该模型在大多数数据集上的性能优于当前最先进的方法。此外，我们分析了由此产生的预测轨迹和学习的注意力分数，以证明BiRRN识别社会互动的优势。
Autonomous mobile robots navigating through human crowds are required to foresee the future trajectories of surrounding pedestrians and accordingly plan safe paths to avoid any possible collision. This paper presents a novel approach for pedestrian trajectory prediction. In particular, we developed a new method based on an encoder–decoder framework using bidirectional recurrent neural networks (BiRNN). The difficulty of incorporating social interactions into the model has been addressed thanks to the special structure of BiRNN enhanced by theattention mechanism, a proximity-independent model of the relative importance of each pedestrian. The main difference between our and the previous approaches is that BiRNN allows us to employs information on the future state of the pedestrians. We tested the performance of our method on several public datasets. The proposed model outperforms the current state-of-the-art approaches on most of these datasets. Furthermore, we analyze the resulting predicted trajectories and the learned attention scores to prove the advantages of BiRRNs on recognizing social interactions.