A Two-Block RNN-Based Trajectory Prediction From Incomplete Trajectory

Fujii, Ryo; Vongkulbhisal, Jayakorn; Hachiuma, Ryo; Saitō, Hideo

doi:10.1109/access.2021.3072135

Cited by 12 publications

(4 citation statements)

References 44 publications

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“…Both LSTM and GRU are able to deal with long term dependencies in the trajectory sequences, where the vehicle behaviour may be influenced by factors over a long history time. RNNs are also widely used in modelling the historical motion trajectories of vehicles [13]. RNN has good effect on the processing of sequence data, which is very important for vehicle trajectory prediction, the trajectory data has strong sequence.…”

Section: B Deep-learning Based Trajectory Prediction Methodsmentioning

confidence: 99%

Vehicle Trajectory Prediction With Interaction Regions and Spatial–Temporal Attention

Cheng,

Gu,

Qian

et al. 2023

IEEE Access

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Vehicle trajectory prediction is a key technology in autonomous driving, one of the aims of which is to ensure the safety of vehicle travelling and lane changing. Especially in complex traffic scenarios, it is critical to accurately predict the vehicle trajectory so that the automated driving system can make appropriate response actions to help the vehicle driver avoid accidents to the greatest extent possible. Some studies on vehicle trajectory prediction have used recurrent networks (LSTMs) to extract temporal correlations and additional convolutional neural networks (CNNs) to capture spatial correlations. This hybrid model can be added more operations, so we propose a new CNN-LSTM hybrid model containing multi-module, interaction regions and spatial-temporal attention mechanisms (IA-CSTM). In this paper, the traffic scene is divided into multiple interaction regions, and vehicles in different regions have different impacts on the predicted vehicles. The model has multiple modules that deal with vehicle information in different interaction regions. The model will cross-fuse vehicle information in important interaction regions to enhance the model's perception of vehicle information. Multiple modules are linked through a spatialtemporal attention mechanism and capture the relevance of the vehicle in the interaction region to predicted vehicle.

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Section: B Deep-learning Based Trajectory Prediction Methodsmentioning

confidence: 99%

Vehicle Trajectory Prediction With Interaction Regions and Spatial–Temporal Attention

Cheng,

Gu,

Qian

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…With the examples of the successful applications in voice recognition [103,92], machine translation [33,95], and sequential prediction [22,81], RNNs (LSTM, GRU, and their variants) have become a widely used framework for the human motion prediction task. It is normally deemed as sequence-to-sequence (seq2seq) prediction tasks, where RNNs are adopted in solving.…”

Section: Rnn-based Methodsmentioning

confidence: 99%

3D Human Motion Prediction: A Survey

Lyu¹,

Chen²,

Liu³

et al. 2022

Preprint

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3D human motion prediction, predicting future poses from a given sequence, is an issue of great significance and challenge in computer vision and machine intelligence, which can help machines in understanding human behaviors. Due to the increasing development and understanding of Deep Neural Networks (DNNs) and the availability of large-scale human motion datasets, the human motion prediction has been remarkably advanced with a surge of interest among academia and industrial community. In this context, a comprehensive survey on 3D human motion prediction is conducted for the purpose of retrospecting and analyzing relevant works from existing released literature. In addition, a pertinent taxonomy is constructed to categorize these existing approaches for 3D human motion prediction. In this survey, relevant methods are categorized into three categories: human pose representation, network structure design, and prediction target. We systematically review all relevant journal and conference papers in the field of human motion prediction since 2015, which are presented in detail based on proposed categorizations in this survey. Furthermore, the outline for the public benchmark datasets, evaluation criteria, and performance comparisons are respectively presented in this paper. The limitations of the state-of-the-art methods are discussed as well, hoping for paving the way for future explorations.

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“…The problem of trajectory prediction from partial observations owing to the miss-detection of dynamic agents (cars, pedestrians, etc.) which can be brought on by poor image quality or occlusion by other dynamic agents, has been studied by Fujii et al [17]. The standard method for handling the incomplete trajectory problem has been to consider the miss detection cases as anomalies and remove them from the dataset.…”

Section: Related Workmentioning

confidence: 99%

“…Two block RNN [17] 0.363 Brits [11] 0.312 GRU based Encoder-Decoder 0.306 LSTM-GRU Encoder-Decoder (Ours) 0.2264…”

Section: Model L1 Lossmentioning

confidence: 99%

HTIM: A Hybrid Deep Learning Approach For Pedestrian Trajectory Imputation

Barua,

Halder,

Shopnil

et al. 2024

Preprint

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Pedestrian trajectories are crucial for self-driving cars to plan their paths effectively. The sensors implanted in these self-driving vehicles, despite being state-of-the-art ones, often face inaccuracies in the perception of surrounding environments due to technical challenges for adverse weather conditions, interference from other vehicles sensors and electronic devices, and signal reception failure, leading to incompleteness in trajectory data. But for real-time decision making for autonomous driving, the trajectory imputation is no less crucial. Previous attempts to address this issue such as statistical inference and machine learning approaches have shown promise. Yet, the landscape of deep learning is rapidly evolving with new and more robust models emerging. In this research, we have proposed an encoder-decoder architecture, Human Trajectory Imputation Model, coined as HTIM, to tackle these challenges. This architecture aims to fill in the missing parts of pedestrian trajectories. The model has been evaluated using the Intersection drone inD dataset, containing trajectory data at suitable altitudes preserving naturalistic pedestrian behavior with varied dataset sizes. To assess the effectiveness of our model, we have utilized L1, MSE, Quantile and ADE loss. Our experiments have demonstrated that HTIM outperforms the majority of the state-of-the-art methods in this field, thus indicating its superior performance in imputing pedestrian trajectories.

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A Two-Block RNN-Based Trajectory Prediction From Incomplete Trajectory

Cited by 12 publications

References 44 publications

Vehicle Trajectory Prediction With Interaction Regions and Spatial–Temporal Attention

Vehicle Trajectory Prediction With Interaction Regions and Spatial–Temporal Attention

3D Human Motion Prediction: A Survey

HTIM: A Hybrid Deep Learning Approach For Pedestrian Trajectory Imputation

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