Predicting the future trajectories of pedestrians is a challenging problem that has a range of application, from crowd surveillance to autonomous driving. In literature, methods to approach pedestrian trajectory prediction have evolved, transitioning from physics-based models to data-driven models based ...
概述 本文献是一篇文献综述,以自动驾驶载具对外围物体行动轨迹的预测为切入点,介绍了基于运动学(kinematics based)和基于机器学习(learning based)的两大类预测方法。 并选择了基于机器学习的六种具体方法(GP、LSTM、GP LSTM、Character based LST
Pedestrian trajectory prediction is crucial for several applications such as robotics and self-driving vehicles. Significant progress has been made in the past decade thanks to the availability of pedestrian trajectory datasets, which enable trajectory prediction methods to learn from pedestrians' past move...
This paper presents a novel approach for pedestrian trajectory prediction. In particular, we developed a new method based on an encoder鈥揹ecoder framework using bidirectional recurrent neural networks (BiRNN). The difficulty of incorporating social interactions into the model has been addressed thanks ...
In the task of pedestrian trajectory prediction, social interaction could be one of the most complicated factors since it is difficult to be interpreted through simple rules. Recent studies have shown a great ability of LSTM networks in learning social behaviors from datasets, e.g., introducing LS...
目录 概览 1. 描述 :模型基于LSTM神经网络提出新型的Spatio Temporal Graph(时空图),旨在实现在拥挤的环境下,通过将行人 行人,行人 静态物品两类交互纳入考虑,对行人的轨迹做出预测。 2. 训练与测试数据库 1. 数据库:ETH Walking Pedestrian &a
Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction(时空图tansformer网络) 摘要: 了解人群运动动力学对于现实世界的应用是至关重要的,例如监视系统和自动驾驶。这是具有挑战性的,因为它需要有效地建模具有社会意识的人群空间互动和复杂的时间依赖性。我们认为注意力是轨迹预测中最重要的因素。
Pedestrian trajectory prediction plays an important role in both pedestrian collision avoidance systems and autonomous driving. However, most of the previous works have ignored the interaction between traffic participants or only take it into account implicitly based on neural networks, which need a large...
Pedestrian motion trajectory prediction is an important task in intelligent driving, and it can provide a valuable reference for the subsequent path decision of intelligent driving. However, so far, there are only a few models in the field of specific pedestrian motion track prediction in intelligent...
在时间维度上,对每个行人单独考虑,应用temporal Transformer抽取时许相关性; 即使是时许上的Transformer,也提供了比RNN更好的表现; 在空间维度上,引入TGConv--Transformer-based message passing graph convolution mechanism。相较于传统的图卷积抽取行人之间的交互关系,采用TGConv在高人群密度、复杂交互关系的情形下能...