Human trajectory prediction and generation using LSTM models and GANs
Human trajectory prediction and generation using LSTM models and GANs
复制标题
使用 LSTM 模型和 GAN 进行人体轨迹预测和生成
DOI：
10.1016/j.patcog.2021.108136
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
E. Frontoni
中科院分区：
文献类型：
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作者：
L. Rossi;M. Paolanti;R. Pierdicca;E. Frontoni
人类轨迹预测是从自动驾驶汽车到环境设计和规划、从社会感知机器人到智能跟踪系统等多个应用领域的重要课题。这一复杂的学科面临着不同的挑战，如人与空间的相互作用、人与人的相互作用、多模态和概括性。目前，这些挑战，特别是普遍性，还没有完全探索的最先进的作品。这项工作试图通过提出和定义新的方法和指标来帮助理解轨迹来填补这一空白。特别是，基于长短期记忆和生成对抗网络架构的新深度学习模型可用于单峰和多峰环境。这些方法进行了评估与新的误差指标，标准化的标准度量中的一些偏差。测试已经使用新收集的数据集进行了评估，其特征在于比最先进的作品中使用的数据集具有更高的多样性和更低的线性。结果证明，所提出的模型和数据集是可比的，并产生更好的泛化能力比国家的最先进的作品。此外，我们还证明了我们的数据集更好地代表了多模态场景（允许多种可能的行为），并且人类轨迹受到其空间区域的适度影响，并受到其日期和时间的轻微影响。
Human trajectory prediction is an important topic in several application domains, ranging from self-driving cars to environment design and planning, from socially-aware robots to intelligent tracking systems. This complex subject comes with different challenges, such as human-space interaction, human-human interaction, multimodality, and generalizability. Currently, these challenges, especially generalizability, have not been completely explored by state-of-the-art works. This work attempts to fill this gap by proposing and defining new methods and metrics to help understand trajectories. In particular, new deep learning models based on Long Short-Term Memory and Generative Adversarial Network architectures are used in both unimodal and multimodal contexts. These approaches are evaluated with new error metrics, which normalize some biases in standard metrics. Tests have been assessed using newly collected datasets characterized by a higher diversity and lower linearity than those used in state-of-the-art works. The results prove that the proposed models and datasets are comparable to and yield better generalizability than state-of-the-art works. Moreover, we also prove that our datasets better represent multimodal scenarios (allowing for multiple possible behaviors) and that human trajectories are moderately influenced by their spatial region and slightly influenced by their date and time.