Trajectory Prediction Model Research Articles

Advanced obstacle recognition and trajectory prediction for autonomous vehicles using 3D CNN and GRU

Abstract. Recent years have seen a rapid advancement in new energy vehicle technology, which has increased consumer demand for autonomous driving systems. However, current self-driving cars still face many technical barriers. Obstacle detection and obstacle trajectory prediction in dynamic environments is one of the main difficulties of the technique. To improve self-driving cars' capacity for route planning and decision-making in dynamic contexts, this thesis proposes an obstacle identification and trajectory prediction model that combines a gated recurrent unit (GRU) network with a three-dimensional convolutional neural network (3D CNN). The integrated framework utilises the feature recognition capability of 3D CNN and the prediction capability of GRU to accurately predict dynamic obstacles, thereby improving the performance of the model in various types of environments (e.g., visual SLAM). Experimental results based on publicly available datasets (e.g., KITTI and Argoverse) show that the proposed method has significant advantages in dynamic obstacle identification and prediction as well as overall model operation efficiency. The findings show that the integrated framework is useful and successful in advancing the creation of self-driving automobiles.

Hypersonic glide vehicle trajectory prediction based on frequency enhanced channel attention and light sampling-oriented MLP network

Hypersonic Glide Vehicles (HGVs) are advanced aircraft that can achieve extremely high speeds (generally over 5 Mach) and maneuverability within the Earth's atmosphere. HGV trajectory prediction is crucial for effective defense planning and interception strategies. In recent years, HGV trajectory prediction methods based on deep learning have the great potential to significantly enhance prediction accuracy and efficiency. However, it's still challenging to strike a balance between improving prediction performance and reducing computation costs of the deep learning trajectory prediction models. To solve this problem, we propose a new deep learning framework (FECA-LSMN) for efficient HGV trajectory prediction. The model first uses a Frequency Enhanced Channel Attention (FECA) module to facilitate the fusion of different HGV trajectory features, and then subsequently employs a Light Sampling-oriented Multi-Layer Perceptron Network (LSMN) based on simple MLP-based structures to extract long/short-term HGV trajectory features for accurate trajectory prediction. Also, we employ a new data normalization method called reversible instance normalization (RevIN) to enhance the prediction accuracy and training stability of the network. Compared to other popular trajectory prediction models based on LSTM, GRU and Transformer, our FECA-LSMN model achieves leading or comparable performance in terms of RMSE, MAE and MAPE metrics while demonstrating notably faster computation time. The ablation experiments show that the incorporation of the FECA module significantly improves the prediction performance of the network. The RevIN data normalization technique outperforms traditional min-max normalization as well.

STIA-DJANet: Spatial–Temporal Intention-Aware vessel trajectory prediction based on Dual-Joint Attention Network for e-navigation

Harnessing high-precision vessel trajectory prediction holds great promise for enhancing autonomous and safe navigation towards e-navigation. However, many technical researchers and maritime managers realize that only accurate prediction of vessel trajectory is insufficient, extraction and recognition of navigation intentions from vessel around is still crucial issue and gap in current prediction models. In response to solve this shortcoming, we propose an intention-aware model for vessel trajectory prediction, which leverages both of spatial–temporal intentions and dual joint attentions network, namely STIA-DJANet. First, the double intentions of spatial–temporal aware is designed to extract the social relationships between the own vessel trajectory and neighboring trajectories at each timestamp, effectively capturing dynamic changes in their interactions. Second, feature fusion of dual joint attentions for double intentions is proposed to extract navigation features from the intention-aware module, which can be integrated into trajectory prediction. In experiments, numerical results and comparisons on three real-world datasets demonstrate that our framework outperforms state-of-the-art models in terms of the average displacement error (ADE) and final displacement error (FDE) metrics. The average improvements were 42.852% and 44.726%, respectively. Reliable prediction results can significantly advance the development of e-navigation systems development.

GRU-IKF scene taxiing trajectory prediction model based on attention mechanism

Aircraft taxi trajectory prediction helps solve operational problems such as airport taxiing conflicts and long waiting times, ensuring airport safety while improving service levels and increasing airport throughput. In view of the fact that the performance of machine learning models depends on good data sets, a method for predicting taxi trajectories of aircraft on the ground based on attention mechanism, fusion of gated recurrent unit (GRU) and improved Kalman filter algorithm (IKF) is proposed. Firstly, three independent gated recurrent unit networks are used to capture the motion state and temporal dependency of aircraft at future moments, and the attention mechanism is introduced to enhance the ability to extract data difference features and learn the mapping relationship from input to output; then, it is fused with the improved extended Kalman filter to integrate the output results of the neural network into the state prediction and update process to improve the accuracy of the predicted trajectory sequence. Finally, the effectiveness of the model was verified using the actual taxiing trajectory of aircraft at Lukou Airport. The simulation results show that the model can effectively and accurately predict the taxiing trajectory of aircraft on the surface, with an overall mean square error of about 0.00128. Compared with the single recurrent neural network (RNN), long short-term memory network (LSTM) and GRU model, the RMSE is reduced by 72.9%and respectively 39.9%, 54.7%and the prediction time is 40 ms. It can accurately and quickly predict the taxiing trajectory, which helps to reduce the operating load of the airport surface management system.

PTNS: patent citation trajectory prediction based on temporal network snapshots

With the rapid development of science and technology, the pace of development in the knowledge economy is accelerating. Intellectual property, especially patents, is a strategic resource for technological innovation and a crucial support for building an innovative country. Therefore, it is particularly important to predict patents with high value and strong impact from the numerous and uneven-quality patents. However, patent citation behavior involves many uncertainties, and it is difficult to capture its temporal variations effectively. Therefore, this paper proposes a patent citation trajectory prediction model (PTNS) based on temporal network snapshots. It adopts relational graph convolutional networks (R-GCN) to learn the complex relationships among multiple attributes of patents and utilizes bidirectional long short-term memory networks (BiLSTM) to aggregate the temporal evolution differences of patents. Subsequently, principal component analysis (PCA) is used to explore the evolution characteristics of patent citations in depth, thereby capturing the aging effect and the ‘sleeping beauty’ phenomenon. Compared with other baselines, the PTNS performs well. In predicting new, grown, and random patents, the RMSLE decreases by approximately 0.04, 0.14, and 0.18 respectively, while the MALE decreases by approximately 0.04, 0.12, and 0.16 respectively.

Probabilistic model for high-level intention estimation and trajectory prediction in urban environments

To enable successful automated driving, precise behavior prediction of surrounding vehicles is indispensable in urban traffic scenarios. Furthermore, given that a vehicle’s behavior is influenced by the movements of other road users, it becomes crucial to estimate their intentions to anticipate precise future motion. However, the elevated complexity resulting from interdependencies among traffic participants and the uncertainty arising from the object recognition errors present additional challenges. Despite extensive research on inferring intentions, many studies have concentrated on estimating intentions from interactions, resulting in a lack of practicality in urban traffic environments due to low computational efficiency and low robustness against recognition failure of strongly interacting road users. In this paper, we introduce a practical stochastic model for intention estimation and trajectory prediction of surrounding vehicles in automated driving under urban traffic environments. The trajectory is forecasted based on hierarchically computed and probabilistically estimated intentions, which represent an interpretation of vehicle behavior, utilizing only the kinematic state of the focal vehicle and HD maps to ensure real-time performance and enhance robustness. The evaluated results demonstrate that the proposed model surpasses straightforward methods in terms of accuracy while maintaining computational efficiency and exhibits robustness against the recognition failure of traffic participants which strongly influence the focal vehicle.

Evaluation of Trajectory and Destination Prediction Models: A Systematic Classification and Analysis of Methodologies and Recent Results

Predicting trajectories and destinations is of considerable relevance in the context of urban mobility, as it can be useful for suggesting detours, avoiding congestion, and optimizing people's commutes. Therefore, this research performs a classification and analysis of trajectory and destination prediction models in articles published from 2017 to 2023. These models were mapped considering: authors; the existence of more than one geographic scenario; the type of forecast; the use of semantic and contextual data; and description of the algorithms. The result consists of discussions of representative works, based on classification, with grouping of techniques. Furthermore, there is a focus on works that used contextual and/or semantic data, from which another framework was developed, specifying the titles of the articles, and whether the methodology involved the use of points or areas of interest, and a reference to how they were generated. This focus expands the previous framework, specifying the differences of a portion of published studies.

A Novel Trajectory Prediction Method Based on CNN, BiLSTM, and Multi-Head Attention Mechanism

A four-dimensional (4D) trajectory is a multi-dimensional time series that embodies rich spatiotemporal features. However, its high complexity and inherent uncertainty pose significant challenges for accurate prediction. In this paper, we present a novel 4D trajectory prediction model that integrates convolutional neural networks (CNNs), bidirectional long short-term memory networks (BiLSTMs), and multi-head attention mechanisms. This model effectively addresses the characteristics of aircraft flight trajectories and the difficulties associated with simultaneously extracting spatiotemporal features using existing prediction methods. Specifically, we leverage the local feature extraction capabilities of CNNs to extract key spatial and temporal features from the original trajectory data, such as geometric shape information and dynamic change patterns. The BiLSTM network is employed to consider both forward and backward temporal orders in the trajectory data, allowing for a more comprehensive capture of long-term dependencies. Furthermore, we introduce a multi-head attention mechanism that enhances the model’s ability to accurately identify key information in the trajectory data while minimizing the interference of redundant information. We validated our approach through experiments conducted on a real ADS-B trajectory dataset. The experimental results demonstrate that the proposed method significantly outperforms comparative approaches in terms of trajectory estimation accuracy.

Iterative Trajectory Prediction Model Based on Interactive Agent

Iterative Trajectory Prediction Model Based on Interactive Agent

Multimodal vehicle trajectory prediction and integrated threat assessment algorithm based on adaptive driving intention

As autonomous driving and connected communication technologies advance swiftly, vehicle trajectory prediction has become increasingly significant. The motion of a vehicle is contingent not only on its historical trajectory but is also subject to the influence of surrounding vehicles, thereby exhibiting intricate social and temporal interdependencies. Furthermore, the inherent randomness and uncertainty in driver behavior render vehicle trajectory prediction inherently multimodal, a factor that is frequently neglected in current research. Against this backdrop, a multimodal vehicle trajectory prediction (MTP) model based on an encoder-decoder architecture is proposed to hierarchically extract historical features of vehicles. The model consists of five key components: temporal feature encoder module, spatial interaction module, spatial-temporal dependence module, driving intention fusion module and multimodal trajectory output module. Experiments on the NGSIM dataset show that the predictive performance of the model has been improved to varying degrees, especially at 3–5 s, where the improvement is more significant. Compared with state-of-the-art models, the Root Mean Square Error (RMSE) error at 5 s time horizon is 3.38 m on NGSIM dataset, which represents a 25 % improvement. To measure the safety of predicted trajectories, we propose a comprehensive threat assessment model that combines collision time (TTC), headway (TH) and time to lateral collision (TLC) metrics based on safe distance theory. This model not only evaluates the longitudinal collision threat in the following state, but also evaluates the lateral collision threat during driving maneuvers in multi lane scenarios, thereby comprehensively improving the safety of vehicle driving. This research also offers new perspectives and insights for the development of autonomous driving.

A dual-level graph attention network and transformer for enhanced trajectory prediction under road network constraints

Predicting vehicle future trajectories enables various Location-Based Services (LBS), including traffic flow monitoring, vehicle route planning, etc. Vehicles are constrained to predefined road networks. Their traveling behaviors are influenced by both their own attributes and road network characteristics hidden in other vehicles’ travel histories. Most of the existing methods fail to sufficiently leverage such collaborative trajectory data under road network constraints. This paper proposes a novel model, called HGT-RN (Hierarchical Graph Attention Network and Transformer Networks for Enhanced Trajectory Prediction under Road Network Constraints), which exploits all vehicles’ trajectories and integrates road network constraints. Specifically, HGT-RN creates a dual-level Graph Attention Network (GAT) based on global traffic flow and local individual trajectory graph to learn trajectory embeddings: (i) Weighted-directional global traffic flow graph and Weighted-Directional GAT to learn the global-level trajectory embedding by aggregating the neighbors’ embeddings based on the spatial transitional relationships among road intersections over all vehicles’ trajectories, considering the preference of current trajectory; and (ii) Local individual trajectory graph attention network to learn the local-level trajectory embedding by modeling the transitions within the current trajectory at each intersection. When incorporating the trajectory embeddings into a transformer model for future trajectory prediction, a road network topology-aware loss function is designed to improve accuracy. Extensive experiments conducted on three city-scale real-world taxi trajectory datasets demonstrate that HGT-RN significantly outperforms existing baseline models. The code is available at https://github.com/zjy9826/HGT-RN.

A Novel Hypersonic Target Trajectory Estimation Method Based on Long Short-Term Memory and a Multi-Head Attention Mechanism.

To address the complex maneuvering characteristics of hypersonic targets in adjacent space, this paper proposes an LSTM trajectory estimation method combined with the attention mechanism and optimizes the model from the information-theoretic perspective. The method captures the target dynamics by using the temporal processing capability of LSTM, and at the same time improves the efficiency of information utilization through the attention mechanism to achieve accurate prediction. First, a target dynamics model is constructed to clarify the motion behavior parameters. Subsequently, an LSTM model incorporating the attention mechanism is designed, which enables the model to automatically focus on key information fragments in the historical trajectory. In model training, information redundancy is reduced, and information validity is improved through feature selection and data preprocessing. Eventually, the model achieves accurate prediction of hypersonic target trajectories with limited computational resources. The experimental results show that the method performs well in complex dynamic environments with improved prediction accuracy and robustness, reflecting the potential of information theory principles in optimizing the trajectory prediction model.

MmSTCT: spatial–temporal convolution transformer network considering driving intention for multimodal vehicle trajectory prediction of highway

Due to the dynamic nature of traffic flow and the uncertainty of drivers' maneuvers, multimodal trajectory prediction remains challenging. In this paper, a multimodal vehicle trajectory prediction model is proposed based on the encoder-decoder structure, utilizing a spatial-temporal dilated causal convolutional transformer network for the hierarchical extraction of driving intention and spatial interaction features. Specifically, temporal information is extracted from the initial motion states by utilizing a bi-directional long short-term memory network. Subsequently, the social interaction module and spatial-temporal dependency module are introduced to model the interactive influences among surrounding vehicles and the target vehicle at the same timestamp as well as at different timestamps. Then adaptive fusion of driving intention is performed to generate multiple queries for accommodating the output of multimodal trajectory prediction module in a non-autoregressive manner. The effectiveness of the proposed model is demonstrated through experimental evaluations on the NGSIM, HighD and CitySim datasets.

Vehicle trajectory prediction based on cross-attention and multilevel spatio-temporal features

Accurately predicting vehicle trajectories is crucial for motion planning in autonomous driving. Some existing trajectory prediction models employ Long Short-Term Memory (LSTM) networks to encode trajectory information, but this approach may lead to information loss. Additionally, the sparsity of the grid representation may limit the model’s ability to extract vehicle interaction features. To address these issues, this paper proposes a vehicle trajectory prediction model based on cross-attention and multilevel spatio-temporal features. The proposed model incorporates a spatio-temporal feature extraction module designed to capture the dynamic evolution of vehicle states across both time and space. Furthermore, it employs a temporal cross-attention module that reuses hidden states to select spatio-temporal features closely related to the target vehicle’s historical state, thus compensating for the temporal information loss during the encoding process. Additionally, a spatial cross-attention module, informed by social context, is deployed to analyze the dynamic interactions among vehicles. This module filters spatio-temporal interaction features with a significant impact on the target vehicle’s trajectory. The model is trained and tested using the NGSIM dataset and the highD dataset. Compared with other models, the model presented in this paper shows higher prediction accuracy in long-term trajectory prediction.

Vehicle trajectory prediction method integrating spatiotemporal relationships with hybrid time-step scene interaction

In vehicle trajectory prediction, constructing the interactive relationships among vehicles within the traffic environment poses a significant challenge. Existing models predominantly focus on temporal dependencies within vehicle histories and spatial correlations among neighboring vehicles, overlooking the continuous influence of historical vehicle states on the current time step and the interplay of multiple sequences over time. To address these limitations, we propose a method for multimodal vehicle trajectory prediction that integrates Hybrid Time-step Scene Interaction (HTSI) into the spatiotemporal relationships. Firstly, we introduce the HTSI module, comprising Multi-step Temporal Information Aggregation (MTIA) and Single-step Temporal Information Aggregation (STIA) methods. MTIA utilizes multi-head attention mechanisms to capture temporal dependencies between consecutive frames, thereby generating new time series amalgamating the ongoing influence of historical time states on the current timestamp. Simultaneously, STIA employs multi-head attention mechanisms to capture the spatial dimension weights of multiple time series and, by aggregating spatial interaction features at each timestamp, generates new time series fused with spatial interaction influences. Subsequently, feature extraction is performed through LSTM layers. Moreover, we propose an improved DIPM pooling module, improving the model’s long-term prediction capability by selectively reusing historical hidden states. Ultimately, based on training results from the HighD and NGSIM datasets, our model demonstrates significant advantages in long-term prediction compared to other state-of-the-art trajectory prediction models. Specifically, within the 5 s prediction window, the model achieved a root mean square error (RMSE) of 2.79 m on the NGSIM dataset, representing a 33.62% improvement over the baseline model’s average accuracy. Additionally, on the HighD dataset, the model attained an RMSE of 2.16 m, reflecting a 33.43% enhancement. The crucial code can be obtained from the provided link: https://github.com/gyhhq/Prediction-trajectory .

TrajectoryNAS: A Neural Architecture Search for Trajectory Prediction.

Autonomous driving systems are a rapidly evolving technology. Trajectory prediction is a critical component of autonomous driving systems that enables safe navigation by anticipating the movement of surrounding objects. Lidar point-cloud data provide a 3D view of solid objects surrounding the ego-vehicle. Hence, trajectory prediction using Lidar point-cloud data performs better than 2D RGB cameras due to providing the distance between the target object and the ego-vehicle. However, processing point-cloud data is a costly and complicated process, and state-of-the-art 3D trajectory predictions using point-cloud data suffer from slow and erroneous predictions. State-of-the-art trajectory prediction approaches suffer from handcrafted and inefficient architectures, which can lead to low accuracy and suboptimal inference times. Neural architecture search (NAS) is a method proposed to optimize neural network models by using search algorithms to redesign architectures based on their performance and runtime. This paper introduces TrajectoryNAS, a novel neural architecture search (NAS) method designed to develop an efficient and more accurate LiDAR-based trajectory prediction model for predicting the trajectories of objects surrounding the ego vehicle. TrajectoryNAS systematically optimizes the architecture of an end-to-end trajectory prediction algorithm, incorporating all stacked components that are prerequisites for trajectory prediction, including object detection and object tracking, using metaheuristic algorithms. This approach addresses the neural architecture designs in each component of trajectory prediction, considering accuracy loss and the associated overhead latency. Our method introduces a novel multi-objective energy function that integrates accuracy and efficiency metrics, enabling the creation of a model that significantly outperforms existing approaches. Through empirical studies, TrajectoryNAS demonstrates its effectiveness in enhancing the performance of autonomous driving systems, marking a significant advancement in the field. Experimental results reveal that TrajcetoryNAS yields a minimum of 4.8 higger accuracy and 1.1* lower latency over competing methods on the NuScenes dataset.

Ship Trajectory Prediction Model Based on Improved Bi-LSTM

Ship Trajectory Prediction Model Based on Improved Bi-LSTM

GSTGM: Graph, spatial–temporal attention and generative based model for pedestrian multi-path prediction

Pedestrian trajectory prediction in urban environments has emerged as a critical research area with extensive applications across various domains. Accurate prediction of pedestrian trajectories is essential for the safe navigation of autonomous vehicles and robots in pedestrian-populated environments. Effective prediction models must capture both the spatial interactions among pedestrians and the temporal dependencies governing their movements. Existing research primarily focuses on forecasting a single trajectory per pedestrian, limiting its applicability in real-world scenarios characterised by diverse and unpredictable pedestrian behaviours. To address these challenges, this paper introduces the Graph Convolutional Network, Spatial–Temporal Attention, and Generative Model (GSTGM) for pedestrian trajectory prediction. GSTGM employs a spatiotemporal graph convolutional network to effectively capture complex interactions between pedestrians and their environment. Additionally, it integrates a spatial–temporal attention mechanism to prioritise relevant information during the prediction process. By incorporating a time-dependent prior within the latent space and utilising a computationally efficient generative model, GSTGM facilitates the generation of diverse and realistic future trajectories. The effectiveness of GSTGM is validated through experiments on real-world scenario datasets. Compared to the state-of-the-art models on benchmark datasets such as ETH/UCY, GSTGM demonstrates superior performance in accurately predicting multiple potential trajectories for individual pedestrians. This superiority is measured using metrics such as Final Displacement Error (FDE) and Average Displacement Error (ADE). Moreover, GSTGM achieves these results with significantly faster processing speeds.

Block-based construction worker trajectory prediction method driven by site risk

Different from pedestrian trajectory prediction, construction worker trajectories are usually affected by risks and have complex movement patterns. Track point-based prediction methods require high prediction accuracy for safety management. This paper presents a block-based construction worker trajectory prediction method driven by site risk. First, the construction site is divided into multiple blocks and the site safety risk within different blocks is quantified. Second, stopping, large and small turning segments in worker's trajectory are detected to divide the dataset. Finally, a transformer-based trajectory prediction model for construction workers is developed and trained separately for different datasets. The worker's next go-to block and the duration within the next block are predicted. The results show that the accuracy of block direction prediction within 120° reach 93%, and the error of duration prediction can be 0.52 s. The study has theoretical and practical value for promoting block-based safety management and enhancing safety proactivity.

A Pedestrian Trajectory Prediction Model for Right-Turn Unsignalized Intersections Based on Game Theory

A Pedestrian Trajectory Prediction Model for Right-Turn Unsignalized Intersections Based on Game Theory