HGSCO: Heterogeneous Graph Structure Contrast Optimization for Trajectory Prediction

In the field of trajectory prediction, when new scene data is continuously introduced, the system may forget the historical trajectory information that has been learned before, leading to the phenomenon of decreasing the prediction accuracy of previous scenes, which is called catastrophic forgetting. The Multi-Memo Net framework proposed in this paper is an instance-based approach that stores typical trajectories through a memory bank thereby establishing a direct link between the current and historical scenes. During training, self-encoders and decoders are used to efficiently memorize the feature expressions of past trajectories and future intentions; during prediction, the controller finds similar scenes that have occurred in history in the memory bank based on the past trajectories and contextual information for trajectory prediction, and ultimately obtains a multimodal trajectory prediction through K-means clustering, and while predicting, the model can keep updating the content of the memory bank to learn, thus iteratively optimizing the model prediction performance. Thus, the model prediction performance is iteratively optimized. Compared with baseline models such as Kalman filter and Memory Augmented Networks for Multiple Trajectory Prediction (MANTRA) model, the method in this paper has significant improvement in Average Displacement Error (ADE) and Final Displacement Error (FDE) metrics.

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.

Accurate and fast prediction of pedestrian trajectory is very important. However, the interaction between pedestrians is complex, pedestrians are affected not only by their own motion but also by the neighboring pedestrians. Some methods do not extract the pedestrian motion features completely. It is easy to ignore a few of important features of pedestrians. The Spatial-Temporal Attention Graph Convolutional Network (ST-AGNN) for pedestrian trajectory prediction is proposed in this paper. ST-AGNN model can extract the spatial interaction features of each time step and stack the spatial features to obtain the spatial-temporal features. It can predict the future trajectory by using Temporal Convolutional Network (TCN). We evaluate the average displacement error (ADE) and final displacement error (FDE) of the proposed method on ETH and UCY datasets. The experimental results show that the proposed method is effective in accuracy and efficiency.

Aiming at the problems of large simplification and low accuracy of traditional trajectory prediction models, combined with the characteristics of time sequence, continuity, and interactivity of UUV trajectories, an unknown UUV trajectory prediction method based on a gated unit recursive (GRU) neural network model is proposed. The MinMaxScaler method is used to normalize the trajectory data; in order to improve the prediction accuracy, the adaptive parameter adjustment algorithm and adam algorithm are used to optimize the network structure of the established GRU-based trajectory prediction model, without human intervention for parameter adjustment. Finally, the trajectory prediction accuracy of GRU trajectory prediction model and BP trajectory prediction model are analyzed through simulation experiments. The experimental results show that the root mean square error is about 3.96m on the x-axis and 1.00m on the y-axis. The trajectory prediction model based on GRU has faster prediction speed and higher prediction accuracy for unknown UUV trajectory after bearings-only detection.

This article introduces a novel model for low-quality pedestrian trajectory prediction, the social nonstationary transformers (NSTransformers), that merges the strengths of NSTransformers and spatiotemporal graph transformer (STAR). The model can capture social interaction cues among pedestrians and integrate features across spatial and temporal dimensions to enhance the precision and resilience of trajectory predictions. We also propose an enhanced loss function that combines diversity loss with logarithmic root mean squared error (log-RMSE) to guarantee the reasonableness and diversity of the generated trajectories. This design adapts well to complex pedestrian interaction scenarios, thereby improving the reliability and accuracy of trajectory prediction. Furthermore, we integrate a generative adversarial network (GAN) to model the randomness inherent in pedestrian trajectories. Compared to the conventional standard Gaussian distribution, our GAN approach better simulates the intricate distribution found in pedestrian trajectories, enhancing the trajectory prediction's diversity and robustness. Experimental results reveal that our model outperforms several state-of-the-art methods. This research opens the avenue for future exploration in low-quality pedestrian trajectory prediction.

Pedestrian trajectory prediction is fundamental to a wide range of scientific research work and industrial applications. Most of the current advanced trajectory prediction methods incorporate context information such as pedestrian neighbourhood, labelled static obstacles, and the background scene into the trajectory prediction process. In contrast to these methods which require rich contexts, the method in our paper focuses on predicting a pedestrian's future trajectory using his/her observed part of the trajectory only. Our method, which we refer to as LVTA, is a Location-Velocity-Temporal Attention LSTM model where two temporal attention mechanisms are applied to the hidden state vectors from the location and velocity LSTM layers. In addition, a location-velocity attention layer embedded inside a tweak module is used to improve the predicted location and velocity coordinates before they are passed to the next time step. Extensive experiments conducted on three large benchmark datasets and comparison with eleven existing trajectory prediction methods demonstrate that LVTA achieves competitive prediction performance. Specifically, LVTA attains 9.19 pixels Average Displacement Error (ADE) and 17.28 pixels Final Displacement Error (FDE) for the Central Station dataset, and 0.46 metres ADE and 0.92 metres FDE for the ETH&UCY datasets. Furthermore, evaluation on using LVTA to generate trajectories of different prediction lengths and on new scenes without the need of retraining confirms that it has good generalizability.

Accurate pedestrian trajectory prediction is crucial for applications such as autonomous driving and crowd surveillance. This paper proposes the OV-SKTGCNN model, an enhancement to the Social-STGCNN model, aimed at addressing its low prediction accuracy and limitations in dealing with forces between pedestrians. By rigorously dividing monocular and binocular overlapping visual regions and utilizing different influence factors, the model pedestrian interactions more realistically. The Kolmogorov-Arnold Networks (KANs) combined with Temporal Convolutional Networks (TCNs) greatly improve the ability to extract temporal features. Experimental results on the ETH and UCY datasets demonstrate that the model reduces the Final Displacement Error (FDE) by an average of 23% and the Average Displacement Error (ADE) by 18% compared to Social-STGCNN. The proposed OV-SKTGCNN model demonstrates improved prediction accuracy and better captures the subtleties of pedestrian movements.

In the rapidly evolving global shipping industry, accurate vessel trajectory prediction is essential for effective maritime traffic management. However, the inherent uncertainties in vessel voyages pose significant challenges for existing methods in achieving precise long-term predictions. Inspired by the success of large language models (LLMs) in natural language processing, we propose SEMINT—a novel framework that integrates SEMantic cognition and INTent context-awareness for long-term vessel trajectory prediction. SEMINT leverages LLMs to semantically interpret vessel navigation behaviors and infer multiple voyage intents. The final intent inference is derived from the consensus among multiple reasoning chains, which are then combined with historical preferences to form an intent context. Guided by this context, a Transformer-based trajectory prediction model captures the dependencies between critical waypoints and voyage intent, thereby achieving stable long-term numerical predictions. SEMINT combines the advantages of LLMs and task-specific deep learning models, transforming voyage uncertainties into high-level intent diversity and simplifying the learning process for long-term movement patterns. Experiments on real-world datasets show that SEMINT outperforms previous methods, reducing average and final displacement errors by 28.47% and 31.59%, respectively.

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.

Trajectory prediction for challenging scenarios has always been a significant problem in the field due to the complexity of dynamic scenarios and interactions. Furthermore, there is often a dynamic gap between evaluating and validating methods on fixed datasets and real driving scenarios. This letter forms part of a series of reports emanating from the IEEE Transactions on Intelligent Vehicles's Decentralized and Hybrid Workshops (DHW) dedicated to the field of Scenarios Engineering. Our research proposes a scenario engineering-based calibration and validation framework for trajectory prediction of autonomous vehicles to more effectively validate the performance of the method in challenging scenarios. First, Scenarios Engineering (SE) uses OpenSCENARIO and real dataset to generate high-definition maps for challenging driving scenarios. Then, the vectorization approach is employed to extract contextual details from the scene and agent trajectory information from the HD map, and the graph neural network is used to model the high-order interaction to realize the interactive trajectory prediction. Compared with the traditional method, the trajectory prediction can be calibrated through SE so that the prediction process can use more traffic information and attribute characteristics, and improve the evaluation index of prediction. The DHW discusses a practical case to verify the potential of the trajectory prediction framework based on scenarios generation in improving the authenticity and accuracy of trajectory prediction.

Data-driven trajectory prediction with weather uncertainties: A Bayesian deep learning approach

Aircraft trajectory prediction is a challenging problem in air traffic control, especially for conflict detection. Traditional trajectory predictors require a variety of inputs such as flight-plans, aircraft performance models, meteorological forecasts, etc. Many of these data are subjected to environmental uncertainties. Further, limited information about such inputs, especially the lack of aircraft tactical intent, makes trajectory prediction a challenging task. In this work, we propose a deep learning model that performs trajectory prediction by modeling and incorporating aircraft tactical intent. The proposed model adopts the encoder-decoder architecture and makes use of the convolutional layer as well as Gated Recurrent Units (GRUs). The proposed model does not require explicit information about aircraft performance and wind data. Results demonstrate that the provision of enriched aircraft intent, together with appropriate model design, could improve the prediction error up to 30% at a prediction horizon of 10 minutes (from 4.9 nautical miles to 3.4 nautical miles). The model also guarantees the mean error growth rate with increasing look-ahead time to be lower than 0.2 nautical miles per minute. In addition, the model offers a very low variance in the prediction, which satisfies the variance-standard specified by EUROCONTROL (EU Organization for Safety and Navigation of Air Traffic) for trajectory predictors. The proposed model also outperforms the state-of-the-art trajectory prediction model, where the Root Mean Square Error (RMSE) is reduced from 0.0203 to 0.0018 for latitude prediction, and from 0.0482 to 0.0021 for longitude prediction in a single prediction step of 15 seconds look-ahead. We showed that the pre-trained model on ADS-B data maintains its high performance, in terms of cross-track and along-track errors, when being validated in the Bluesky Air Traffic Simulator. The proposed model would significantly improve the performance of conflict detection systems where such trajectory prediction models are needed.

High-performance trajectory prediction is at the core of strategic airspace capacity and safety enhancement. Current state-of-the-art trajectory prediction models are based on a three-dimensional point-mass model, using often predefined settings from existing databases rather than real-time information available onboard the aircraft to determine aircraft dynamics. As a result trajectory prediction performance is limited by the accuracy of these settings. This paper addresses this limitation and proposes a high-performance four-dimensional trajectory prediction model for civil aircraft. The model includes a new flight-control system and an enhanced flight-script. The latter incorporates new taxonomy and content enabling better definition of aircraft intent. The performance of the trajectory prediction model is assessed using data acquired during a real flight trial, and shown to be significantly better than the current models. It has the potential to meet the required navigation performance for departure, en-route and non-precision-approach phases of flight.

Trajectory prediction is one of the key technologies of the autonomous driving system. The accurate and efficient prediction can assist the autonomous driving system in making correct decisions and ensuring driving safety. This paper summarizes the research on trajectory prediction for autonomous vehicles based on deep learning. First, trajectory prediction models’ input representation and output types are analyzed. Then, deep learning-based trajectory prediction methods are reviewed with their advantages and limitations. Finally, future research directions are proposed to address the shortcomings of existing methods in terms of multi-agent interaction modeling and generalization capability.

Human trajectory prediction is a crucial yet challenging problem, which is of fundamental importance to robotics and autonomous driving vehicles. The core challenge lies in effectively modeling the socially aware spatial interaction and complex temporal dependencies among crowds. However, previous methods usually model spatial and temporal information separately and only use individual features, while ignoring group-level motion characteristics. We propose a novel trajectory prediction framework termed GA-STT, a group aware spatial-temporal transformer network to address these issues. Specifically, we first get the individual representations supervised by group-based annotations. Then, we model the complex spatial-temporal interactions with spatial and temporal transformers separately and fuse spatial-temporal embedding through the cross-attention mechanism. Results on the publicly available ETH/UCY datasets show that our model outperforms the state-of-the-art method by 19.4% in average displacement error(ADE) and 16.9% in final displacement error(FDE) and successfully predicts complex spatial-temporal interactions.