AST-GNN: An attention-based spatio-temporal graph neural network for Interaction-aware pedestrian trajectory prediction

Trajectory prediction of cyclist based on spatial‐temporal multi‐graph network in crowded scenarios

“…There are also 46% singletons in the dataset. Metrics. For comparison, we select several prediction methods as baselines including: (1) Social‐LSTM, a modified LSTM model with social pooling layer, (2) Bi‐LSTM [11], a cyclist trajectory prediction method using bidirectional recurrent neural network, (3) Mi‐LSTM [9], an LSTM model using multiple interaction clues, (4) SR‐LSTM [3], an LSTM model utilizing the current states of all participants in the crowd through a message passing mechanism, (5) Social‐GAN [12], a generative adversarial networks based encoder decoder framework for trajectory prediction, (6) STGCNN [5], a unified Graph Convolutional Neural Network for pedestrian trajectory forecasting, (7) AST‐GNN [13], an attention‐based spatio‐temporal graph neural network for pedestrian trajectory prediction, (8) Graph‐TCN [14], a spatio‐temporal interaction modelling for human trajectory prediction.Their performances are evaluated on the non‐vehicle dataset in terms of the Average Displacement Error (ADE) and the Final Displacement Error (FDE) in Tables 1 and 2, respectively [2]. The evaluation is conducted according to a fivefold cross validation, which is widely used in the trajectory prediction.…”

Graph Neural Network for Air Quality Prediction: A Case Study in Madrid

“…In addition, many authors have recently been drawn to the advantages of a graph neural network in areas such as traffic flow prediction [14]- [17], parking availability prediction [18], pedestrian trajectories prediction [19], [20], urban vehicle emission prediction [21], wind speed prediction [22], weather prediction [23] and solar irradiance prediction [24]. The air quality domain, among others, has also benefited from these advantages, and various authors have used graph neural networks to forecast air quality.…”

MALS-Net: A Multi-Head Attention-Based LSTM Sequence-to-Sequence Network for Socio-Temporal Interaction Modelling and Trajectory Prediction

“…Transformers have been introduced to the literature with the promise of tackling the issue of long-term temporal correlation as well as parallelizing the decoding process. Inspired by its distinct attention mechanism, various attention-based techniques have been adopted in [ 1 , 7 ]. However, the multi-headed attention mechanism, originally proposed in the traditional transformer [ 8 ], has not extensively been explored in the highway trajectory prediction problem, mainly due to the problem of accumulative errors resulting from the autoregressive decoding procedure of transformers [ 9 ].…”