Multi-PPTP: Multiple Probabilistic Pedestrian Trajectory Prediction in the Complex Junction Scene

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.

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

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.

Multi-camera video surveillance has been widely applied in crowd statistics and analysis in smart city scenarios. Most existing studies rely on appearance or motion features for cross-camera trajectory tracking, due to the changing asymmetric perspectives of multiple cameras and occlusions in crowded scenes, resulting in low accuracy and poor tracking performance. This paper proposes a tracking method that fuses appearance and motion features. An implicit social model is used to obtain motion features containing spatio-temporal information and social relations for trajectory prediction. The TransReID model is used to obtain appearance features for re-identification. Fused features are derived by integrating appearance features, spatio-temporal information and social relations. Based on the fused features, multi-round clustering is adopted to associate cross-camera objects. Exclusively employing robust pedestrian reidentification and trajectory prediction models, coupled with the real-time detector YOLOX, without any reliance on supplementary information, an IDF1 score of 70.64% is attained on typical datasets derived from AiCity2023.

Non-probability sampling network based on anomaly pedestrian trajectory discrimination for pedestrian trajectory prediction

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.

Pedestrian trajectory prediction is important in various applications such as driverless vehicles, social robots, intelligent tracking systems and space planning. Existing methods focus on analysing the influence of neighbours but ignore the effect of the intended destinations of pedestrians which also plays a key role in route planning. In this paper, we propose a novel two- stage trajectory prediction method to yield multiple prediction trajectories with different probabilities towards different destination regions in the scene. Our method, which we refer to as Bi-Prediction, uses a bidirectional LSTM architecture to automatically classify trajectories into a small number of route classes before trajectory prediction. We have evaluated our method against two baseline methods and three state-of-art methods on two benchmark datasets. Our experimental results show that the extra classification stage improves the accuracy of the predicted trajectories.

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.

For intelligent vehicles, accurate prediction of the trajectories of surrounding vehicles is an important basis for identifying potential risks in advance and making optimal decisions. However, unsignalized roundabouts have multiple entrance and exit ramps with uncertain angles and numbers, which leads to more diverse driving intentions and more complex multi-vehicle interactions of surrounding vehicles, which significantly reduces the accuracy of trajectory prediction and even causes wrong decisions and serious collision accidents. To this end, this paper innovatively proposes a method for predicting the trajectory of surrounding vehicles in unsignalized roundabouts based on global-local historical information fusion (TP-GLIF). From a global perspective, a scene-adaptive dynamic and static feature information encoder is constructed, and a multi-head attention mechanism is introduced to capture environmental information closely related to driving intention, so that the model always focuses on the dynamic and static features that are most beneficial to improving prediction accuracy. From a local perspective, a novel dynamic heterogeneous graph attention multi-vehicle interaction encoder is constructed, and a novel hierarchical Gaussian mixture model-hidden Markov model-support vector machine driving intention recognition algorithm is designed to guide prediction decoding. A long short-term decoder LS-GRU is designed to decode the surrounding vehicle trajectories under the influence of complex road structure features, complex multi-vehicle interaction features and diverse driving intention features in different time periods to reduce the cumulative error. Experimental results show that TP-GLIF can achieve accurate trajectory prediction in different roundabout scenes, and has good computational efficiency and generalization ability.

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

Trajectory prediction is a challenging problem in autonomous driving, due to changes in scene structure and multimodal distributions of future trajectories. This study proposes a novel trajectory prediction model (LGINet). The central innovation of LGINet is the Context-Aware Graph Attention (CAGA) layer, which achieves a balance between handling scene complexity and the ability to capture information, improving prediction performance. Unlike conventional attention mechanisms, CAGA uses Sparsemax to focus on key neighbors, reducing noise and enhancing computational efficiency. The multi-head attention mechanism enhances the ability of the model to capture interactions across both local and long-distance spatial scales. Experiments with the nuScenes dataset show that the proposed LGINet model outperforms existing prediction methods. Specifically, the MinADE 5 value of LGINet is 1.23, the MR 5,2 value is 0.49, and the MinFDE 5 value is 2.39, demonstrating its effectiveness in generating diverse and accurate future trajectories for autonomous driving applications. Qualitative comparisons confirm that LGINet generates diverse trajectories aligned with scene structures and agent motion, highlighting its potential for autonomous driving.

This work remedies the drawbacks of existing methods in pedestrian trajectory prediction for video surveillance. A traditional two-stage approach, which first detects and tracks pedestrians before forecasting their subsequent trajectories, relies solely on historical trajectories to predict future movements, overlooking the rich contextual information within videos. Although some one-stage methods have been introduced to streamline this process, they still struggle with complex model architectures due to their reliance on pixel-wise flow estimation. To resolve these issues, we propose a simple yet effective Transformer-based one-stage model, called PredDETR, that directly anticipates the future trajectories of multiple pedestrians from videos. Following the end-to-end paradigm of DETR, our PredDETR model not only associates the bounding boxes of each pedestrian across subsequent frames but also predicts their future trajectories using an additional predictive decoder. Taking past raw video frames as input, the proposed PredDETR directly forecasts the future locations of each pedestrian in a non-autoregressive manner. The empirical results validate that, despite its simpler design, PredDETR is a compelling method compared to a previous one-stage approach.

Pedestrian trajectory prediction can provide decision support for self-driving cars and robots to avoid potential collision risks. Modelling of social relationships and multimodal prediction are core challenges in pedestrian trajectory prediction. However, there are two problems with existing methods: (1) these methods usually focus on modelling pairwise or group interactions, ignoring the importance of high-order interactions; and (2) existing methods struggle to balance the performance between deterministic and multimodal predictions. To address these issues, we propose a higher-order graph attention network for pedestrian trajectory prediction. Specifically, we first construct spatial and temporal graphs to model social interactions and temporal dependencies. To achieve high-order interaction modelling, we design a higher-order graph attention module to capture pedestrian social and temporal interactions of different orders by stacking multiple layers of graph attention. Subsequently, we design a deformable spatial-temporal sparse module to adaptively eliminate social and temporal redundant interactions. To balance deterministic and multimodal prediction performance, we design a two-stage prediction strategy. The first stage predicts pedestrian deterministic trajectories, and the second stage refines pedestrian deterministic trajectories to complete both deterministic and multimodal predictions. Quantitative analysis on the ETH, UCY, and SDD datasets demonstrates that our method outperforms existing methods in both deterministic and multimodal predictions. Qualitative analysis shows that our method can effectively describe complex social relationships and accomplish reliable trajectory prediction.