HSTA: A Hierarchical Spatio-Temporal Attention Model for Trajectory Prediction

Wu, Ya; Chen, Guang; Li, Zhijun; Zhang, Lijun; Liu, Xiong; Liu, Zhengfa; Knoll, Alois

doi:10.1109/tvt.2021.3115018

Cited by 39 publications

(18 citation statements)

References 40 publications

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“…Among them, Messaoud et al [14] used the multihead attention mechanism to evaluate the relative importance between nearby vehicles and extracted different types of social relationships. Conversely, Wu et al [36] applied the multi-head attention mechanism to capture the complex temporal correlation of each agent independently. More recently, motivated by the fact that the graph convolutional network (GCN) [37] can capture the relative influence and the potential spatial relationships in traffic scenarios, the graph attention network (GAT) [38] has been used in trajectory prediction [39][40][41], extracting the spatial interaction among neighboring agents by assigning different importance to neighbors around the target agent.…”

Section: Attention-based Methods For Trajectory Predictionmentioning

confidence: 99%

Spatial-Temporal Attentive LSTM for Vehicle-Trajectory Prediction

Jiang

Gong

et al. 2022

IJGI

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Vehicle-trajectory prediction is essential for intelligent traffic systems (ITS), as it can help autonomous vehicles to plan a safe and efficient path. However, it is still a challenging task because existing studies have mainly focused on the spatial interactions of adjacent vehicles regardless of the temporal dependencies. In this paper, we propose a spatial-temporal attentive LSTM encoder–decoder model (STAM-LSTM) to predict vehicle trajectories. Specifically, the spatial attention mechanism is used to capture the spatial relationships among neighboring vehicles and then obtain the global spatial feature. Meanwhile, the temporal attention mechanism is designed to distinguish the effects of different historical time steps on future trajectory prediction. In addition, the motion feature of vehicles is extracted to reveal the influence of dynamic information on vehicle-trajectory prediction, and is combined with the local and global spatial features to represent the integrated features of the target vehicle at each historical moment. The experiments were conducted on public highway trajectory datasets—US-101 and I-80 in NGSIM—and the results demonstrate that our model achieves state-of-the-art prediction performance.

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Section: Attention-based Methods For Trajectory Predictionmentioning

confidence: 99%

Spatial-Temporal Attentive LSTM for Vehicle-Trajectory Prediction

Jiang

Gong

et al. 2022

IJGI

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“…They also naturally account for interactions between the ego-vehicle and any other traffic participant. Hierarchical Spatio-Temporal Attention architecture (HSTA) has been recently proposed [43], which activates the utilization of spatial interactions with different weights, and jointly considers the temporal interactions across time steps of all agents. [2] proposes a hierarchical control structure, of which the high-level decision-making integrates two attention modules into a dueling double deep Q network (D3QN-DA), achieving a higher safety rate and average explore distance.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

Explaining a Deep Reinforcement Learning (DRL)-Based Automated Driving Agent in Highway Simulations

et al. 2023

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As deep learning models have become increasingly complex, it is critical to understand their decision-making, particularly in safety-relevant applications. In order to support a quantitative interpretation of an autonomous agent trained through Deep Reinforcement Learning (DRL) in the highway-env simulation environment, we propose a framework featuring three types of views for analyzing data: (i) episode timeline, (ii) frame by frame, and (iii) aggregated statistical analysis, also including heatmaps for a better spatial understanding. Our methodology allowed a novel, consistent description of the behavior of the agent. The main motivator for the taken action is typically the longitudinal distance from the second-closest and, to a lower extent, third-closest vehicle. In the overtakes, also the agent's position in lanes becomes relevant. The analysis identified interesting patterns and an issue in the last frames of an episode, when the agent is unable to overtake the last two vehicles, arguably because of the lack of reference vehicles ahead. We observed a clear differentiation between attention and SHAP values (estimating the importance of each feature for each decision), reflecting the architecture of the neural network, where the first layer implements the attention mechanism, while the deeper ones make the actual decision. Attention focuses on the proximity of the ego, while the decision is taken on a wider horizon, denoting a valuable anticipation capability. To support research, the proposed framework is released as open source.

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“…Gated Fusion. We use a gated fusion unit to fuse spatiotemporal features by adaptively controlling the effect of spatiotemporal attention at each time slot [32]. As shown…”

Section: Spatial Attention Mechanismmentioning

confidence: 99%

“…By referring to the experimental parameter settings in [22,25], we set the ranges of the relevant experimental parameters. The dimensional reference values of the graph convolution module (16,32,64), the historical time window reference values (30 min, 60 min, 90 min), the learning rate reference values (0.1, 0.01, 0.001, 0.0001), the dropout reference values (0.1, 0.2, 0.3, 0.4, 0.5), the batch size reference values (16,32,64,128), and optimization are chosen from SGD and Adam. We find the optimal parameters in the validation by implementing a grid search strategy.…”

Section: Experimental Settingmentioning

confidence: 99%

Multigraph Aggregation Spatiotemporal Graph Convolution Network for Ride-Hailing Pick-Up Region Prediction

Zhang

Wang

et al. 2022

Wireless Communications and Mobile Computing

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The prediction of pick-up regions for online ride-hailing can reduce the number of vacant vehicles on the streets, which will optimize the transportation efficiency of cities, reduce energy consumption and carbon emissions, and increase the income of online ride-hailing drivers. However, traditional studies have ignored the temporal and spatial dependencies among pick-up regions and the effects of similarity of POI attributes in different regions in modelling, making the features of the model incomplete. To address the above problems, we propose a new multigraph aggregation spatiotemporal graph convolutional network (MAST-GCN) model to predict pick-up regions for online ride-hailing. In this paper, we propose a graph aggregation method to extract the spatiotemporal aspects and preference features of spatial graphs, order graphs, and POI graphs. GCN is used on the aggregated graphs to extract spatial dimensional features from graph-structured data. The historical data are sequentially divided into temporal granularity according to the period, and convolution operations are performed on the time axis to obtain the features in the temporal dimension. The attention mechanism is used to assign different weights to features with strong periodicity and strong correlation, which effectively solves the pick-up region prediction problem. We implemented the MAST-GCN model based on the PyTorch framework, stacked with a two-layer spatiotemporal graph convolution module, where the dimension of the graph convolution is 64. We evaluate the proposed model on two real-world large scale ride-hailing datasets. The results show that our method provides significant improvements over state-of-the-art baselines.

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HSTA: A Hierarchical Spatio-Temporal Attention Model for Trajectory Prediction

Cited by 39 publications

References 40 publications

Spatial-Temporal Attentive LSTM for Vehicle-Trajectory Prediction

Spatial-Temporal Attentive LSTM for Vehicle-Trajectory Prediction

Explaining a Deep Reinforcement Learning (DRL)-Based Automated Driving Agent in Highway Simulations

Multigraph Aggregation Spatiotemporal Graph Convolution Network for Ride-Hailing Pick-Up Region Prediction

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