Spatial-Temporal Attentive LSTM for Vehicle-Trajectory Prediction

Jiang, Rui; Xu, Hongyun; Gong, Guo; Kuang, Yong; Liu, Zhikang

doi:10.3390/ijgi11070354

Cited by 15 publications

(5 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because existing studies focus on the spatial interactions of adjacent vehicles regardless of the time dependence. Jiang et al [ 26 ] proposed a spatiotemporal attention LSTM encoder-decoder model to predict vehicle trajectories. Salzmann et al [ 27 ] proposed a modular, graphically structured recurrent model capable of combining dynamic and heterogeneous data from agents and making different predictions depending on the scene structure.…”

Section: Related Workmentioning

confidence: 99%

Multi-Object Trajectory Prediction Based on Lane Information and Generative Adversarial Network

Guo,

Ge,

Shi

2024

Sensors

View full text Add to dashboard Cite

Nowadays, most trajectory prediction algorithms have difficulty simulating actual traffic behavior, and there is still a problem of large prediction errors. Therefore, this paper proposes a multi-object trajectory prediction algorithm based on lane information and foresight information. A Hybrid Dilated Convolution module based on the Channel Attention mechanism (CA-HDC) is developed to extract features, which improves the lane feature extraction in complicated environments and solves the problem of poor robustness of the traditional PINet. A lane information fusion module and a trajectory adjustment module based on the foresight information are developed. A socially acceptable trajectory with Generative Adversarial Networks (S-GAN) is developed to reduce the error of the trajectory prediction algorithm. The lane detection accuracy in special scenarios such as crowded, shadow, arrow, crossroad, and night are improved on the CULane dataset. The average F1-measure of the proposed lane detection has been increased by 4.1% compared to the original PINet. The trajectory prediction test based on D2-City indicates that the average displacement error of the proposed trajectory prediction algorithm is reduced by 4.27%, and the final displacement error is reduced by 7.53%. The proposed algorithm can achieve good results in lane detection and multi-object trajectory prediction tasks.

show abstract

Section: Related Workmentioning

confidence: 99%

Multi-Object Trajectory Prediction Based on Lane Information and Generative Adversarial Network

Guo,

Ge,

Shi

2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Antonios et al [31] extended LSTM for human trajectory and solved the problem that the performance of the Seq2Seq sequence model decreases with the increase of input sequence; they also verified the effectiveness of Seq2Seq sequence model in trajectory modeling and motion pattern prediction. On this basis, different from single-trajectory prediction, STA-LSTM [32] and O-LSTM models take into account the interaction between research objects in a certain space-time region and the impact of environmental information to different degrees, and perform well on ETH and UCY data sets. Wang and Xiao [33] combined the characteristics of the two networks and proposed a CNN-LSTM-SE model for ship trajectory prediction, which performed well on several indexes.…”

Section: Trajectory Predictionmentioning

confidence: 99%

Predicting Vessel Trajectories Using ASTGCN with StemGNN-Derived Correlation Matrix

Zhang,

Chen,

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

This study proposes a vessel position prediction method using attention spatiotemporal graph convolutional networks, which addresses the issue of low prediction accuracy due to less consideration of inter-feature dependencies in current vessel trajectory prediction methods. First, the method cleans the vessel trajectory data and uses the Time-ratio trajectory compression algorithm to compress the trajectory data, avoiding data redundancy and providing feature points for vessel trajectories. Second, the Spectral Temporal Graph Neural Network (StemGNN) extracts the correlation matrix that describes the relationship between multiple variables as a priori matrix input to the prediction model. Then the vessel trajectory prediction model is constructed, and the attention mechanism is added to the spatial and temporal dimensions of the trajectory data based on the spatio-temporal graph convolutional network at the same time as the above operations are performed on different time scales. Finally, the features extracted from different time scales are fused through the full connectivity layer to predict the future trajectories. Experimental results show that this method achieves higher accuracy and more stable prediction results in trajectory prediction. The attention-based spatio-temporal graph convolutional networks effectively capture the spatio-temporal correlations of the main features in vessel trajectories, and the spatio-temporal attention mechanism and graph convolution have certain interpretability for the prediction results.

show abstract

“…Base model Type Accuracy [6], [8], [11], [12], LSTM Independent [13], [14], [16], [17] LSTM Interactive Multimodality [9] LSTM Independent [10] RNN Independent [15], [18] LSTM Interactive Sensor faulttolerance Proposed LSTM Independent…”

Section: Objectivementioning

confidence: 99%

“…Similarly, Yu et al [13] considered road geometries to improve the prediction accuracy in various road environments. Jiang et al [14] focused on the temporal accuracy of trajectory prediction. Other researchers [15]- [18] considered the surrounding nearby vehicles for an improved understanding of future vehicle trajectories.…”

Section: Introductionmentioning

confidence: 99%

Chebyshev Transform-Based Robust Trajectory Prediction Using Recurrent Neural Network

et al. 2022

View full text Add to dashboard Cite

Trajectory prediction is gaining attention as a form of situational awareness because it is an essential component of the support system of autonomous driving, particularly in urban areas. A promising application is cooperative driving automation, where the traffic scene is monitored by roadside sensors with undisrupted views. A critical problem is that these sensors are adversely affected by inclement weather, including drenching rain or large amounts of snow, in which case the reliability of the prediction results can be significantly compromised. To address these problems, this study proposes a framework for robust vehicle-trajectory predictions based on the Chebyshev transform. In the proposed framework, the original trajectory snippets (partial trajectories) are Chebyshev-transformed, and the resulting coefficients form new snippets. The LSTM (long-short term memory) encoder-decoder structure was trained and tested using these new coefficient snippets, which were extracted from a public vehicle trajectory dataset. The performance and robustness of the proposed framework were verified by emulating sensor data that were incomplete as a result of environmental factors. The proposed framework provides stable and accurate longterm trajectory prediction because the Chebyshev transform is robust to incomplete sensor data by virtue of its uniform nature.

show abstract

Spatial-Temporal Attentive LSTM for Vehicle-Trajectory Prediction

Cited by 15 publications

References 43 publications

Multi-Object Trajectory Prediction Based on Lane Information and Generative Adversarial Network

Multi-Object Trajectory Prediction Based on Lane Information and Generative Adversarial Network

Predicting Vessel Trajectories Using ASTGCN with StemGNN-Derived Correlation Matrix

Chebyshev Transform-Based Robust Trajectory Prediction Using Recurrent Neural Network

Contact Info

Product

Resources

About