STGM: Vehicle Trajectory Prediction Based on Generative Model for Spatial-Temporal Features

Zhong, Zhi; Luo, Yutao; Liang, Weiqiang

doi:10.1109/tits.2022.3160648

Cited by 18 publications

(6 citation statements)

References 29 publications

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“…The initial backbone is replaced with inception blocks to take full advantage of the flexible design of the Inception network and validated under different weather and illumination conditions. Recent research (Zhang & Jin, 2023) uses a dynamic mode decomposition method to decompose the STMap into the low‐rank background and sparse foreground components. The preprocessed STMaps are then used to train a deep neural network named ResUNet+.…”

Section: Related Workmentioning

confidence: 99%

Deep spatial‐temporal embedding for vehicle trajectory validation and refinement

Zhang,

Jin,

Piccoli

et al. 2024

Computer aided Civil Eng

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High‐angle cameras are commonly used for trajectory data collection in transportation research. However, without refinement and validation, trajectory data obtained through video processing software may be unreliable, inaccurate, or incomplete. This paper focuses on a critical issue in the field of trajectory data acquisition and analysis—there is still no reliable and fully vetted trajectory dataset in the research community. The current practice for validating video‐based trajectory can be classified as indirect methods and direct methods. Indirect methods of trajectory validation use algorithms to efficiently correct data anomalies without human intervention but may overlook detailed driving behaviors, whereas direct methods involve meticulous manual verification to preserve data fidelity but are labor‐intensive and less scalable. The spatial‐temporal maps (STMaps) method offers an additional layer of verification to affirm the accuracy and reliability of trajectory data. To enhance the performance, the deep spatial‐temporal embedding model is proposed for trajectory instance segmentation on STMaps using the contrastive learning framework. The parity constraints at both pixel and instance levels guide the deep neural network to learn the embedding spaces that can be built on different backbone networks. The reconstructed Next Generation Simulation (NGSIM) highway dataset trajectory dataset is thoroughly validated against manually processed ground truth, and the error‐free NGSIM data are refined to be a reliable resource for transportation research based on car‐following behaviors, lane‐change frequency, consistency, and jerk value measurements.

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Section: Related Workmentioning

confidence: 99%

Deep spatial‐temporal embedding for vehicle trajectory validation and refinement

Zhang,

Jin,

Piccoli

et al. 2024

Computer aided Civil Eng

View full text Add to dashboard Cite

show abstract

“…Other methods extract features directly from traffic map scenes to make predictions of vehicle trajectories, such as [ 28 ], which proposed an ambient attention network that used a graph attention network to extract scene features, thus maintaining the spatial relationship between vehicles and scene structure. Zhong et al [ 29 ] constructed a generative model framework using an auto-encoder to dynamically predict the future trajectories of surrounding vehicles.…”

Section: Related Workmentioning

confidence: 99%

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

Guo,

Ge,

Shi

2024

Sensors

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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.

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“…Using the clustering method and LSTM model, Zhang et al intergrated the intention prediction and trajectory prediction tasks, where the statistical law of trajectory was used to provide prior knowledge for latter anticipation [29]. However, as the number of stacked layers increases, the performance of the RNN-based prediction models is gently ameliorated while the computation sources exponentially grow, which is obviously not suitable for the real-time requirements of autonomous vehicles [30]. In addition, the RNN works in the Euclidean domain, leading to the trickiness to extract and describe several critical features of traffic networks.…”

Section: Related Workmentioning

confidence: 99%

Trajectory Prediction with Attention-Based Spatial–Temporal Graph Convolutional Networks for Autonomous Driving

Li,

Ren,

et al. 2023

Applied Sciences

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Accurate and reliable trajectory prediction is crucial for autonomous vehicles to achieve safe and efficient operation. Vehicles perceive the historical trajectories of moving objects and make predictions of behavioral intentions for a future period of time. With the predicted trajectories of moving objects such as obstacle vehicles, pedestrians, and non-motorized vehicles as inputs, self-driving vehicles can make more rational driving decisions and plan more reasonable and safe vehicle motion behaviors. However, due to traffic environments such as intersection scenes with highly interdependent and dynamic attributes, the task of motion anticipation becomes challenging. Existing works focus on the mutual relationships among vehicles while ignoring other potential essential interactions such as vehicle–traffic rules. These studies have not yet deeply explored the intensive learning of interactions between multi-agents, which may result in evaluation deviations. Aiming to meet these issues, we have designed a novel framework, namely trajectory prediction with attention-based spatial–temporal graph convolutional networks (TPASTGCN). In our proposal, the multi-agent interaction mechanisms, including vehicle–vehicle and vehicle–traffic rules, are meticulously highlighted and integrated into one homogeneous graph by transferring the time-series data of traffic lights into the spatial–temporal domains. Through integrating the attention mechanism into the adjacency matrix, we effectively learn the different strengths of interactive association and improve the model’s ability to capture critical features. Simultaneously, we construct a hierarchical structure employing the spatial GCN and temporal GCN to extract the spatial dependencies of traffic networks. Profiting from the gated recurrent unit (GRU), the scene context in temporal dimensions is further attained and enhanced with the encoder. In such a way, the GCN and GRU networks are fused as a features extractor module in the proposed framework. Finally, the future potential trajectories generation tasks are performed by another GRU network. Experiments on real-world datasets demonstrate the superior performance of the scheme compared with several baselines.

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STGM: Vehicle Trajectory Prediction Based on Generative Model for Spatial-Temporal Features

Cited by 18 publications

References 29 publications

Deep spatial‐temporal embedding for vehicle trajectory validation and refinement

Deep spatial‐temporal embedding for vehicle trajectory validation and refinement

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

Trajectory Prediction with Attention-Based Spatial–Temporal Graph Convolutional Networks for Autonomous Driving

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