TPPO: A Novel Trajectory Predictor With Pseudo Oracle

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.

Pedestrian trajectory prediction is a critical research area with numerous domains, e.g., blind navigation, autonomous driving systems, and service robots. There exist two challenges in this research field: spatio-temporal interaction modeling among pedestrians and the uncertainty of pedestrian trajectories. To tackle these challenges, we propose a spatio-temporal interaction aware and trajectory distribution aware graph convolution network. First, we propose a spatio-temporal interaction aware module that integrates a graph convolutional network and self-attention mechanism to model spatio-temporal interactions among pedestrians. Second, we design a trajectory distribution aware module to learn latent trajectory distribution information from the measured trajectories at observed and future times. This can provide knowledge-rich trajectory distribution information for the multimodality of the predicted trajectories. Finally, to address the problem of the propagation and accumulation of prediction errors, we design a trajectory decoder to generate the multimodal future trajectories. The proposed model is evaluated utilizing videos recorded by a camera sensor in crowded areas and can be applied to predict multiple pedestrians’ future trajectories from in-vehicle cameras. Experimental results demonstrate that the proposed approach can achieve superior results on the average displacement error (ADE) and final displacement error (FDE) metrics to state-of-the-art approaches and can predict socially acceptable future trajectories.

Vehicle trajectory prediction and driving intention recognition are essential technologies for improving safety and optimizing traffic efficiency in autonomous driving systems. Although traditional LSTM models are effective in trajectory prediction, their complexity and computational requirements impede practical implementation efficiency. To overcome this challenge, this study proposes a hybrid model, MTF-GRU, which integrates multi-feature fusion. Initially, the datasets are preprocessed in this study through denoising, feature extraction, and timing extraction to capture vehicle information from single and fused multi-features. Subsequently, a GRU encoding-decoding model is developed. The encoder processes the feature data to generate context vectors, while the decoder employs a combination of recursive and teaching-driven input modes. Furthermore, a teaching rate control mechanism is integrated to dynamically convert context vectors into future trajectories. The proposed model is validated using the NGSIM datasets, demonstrating superior prediction performance with multi-feature inputs outperforming single features by reducing the average endpoint displacement error by 20.5%. Our model also achieves improved accuracy rates, particularly excelling in long-term predictions with an endpoint displacement error of only 2.31 meters at 5 seconds. Moreover, the overall accuracy rate for lane change intention recognition reaches 91.3%. The model's computational efficiency supports practical deployment in real-time autonomous systems, while future efforts will integrate multi-modal sensor data to enhance adaptability in complex urban scenarios and extreme conditions. Further validation will extend to diverse traffic environments and edge computing platforms to optimize real-world robustness.

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.

Meta-IRLSOT++: A meta-inverse reinforcement learning method for fast adaptation of trajectory prediction networks

Predicting and planning the future trajectories of various traffic participants is an important task with multiple applications, including autonomous vehicles, service robots, and intelligent transportation. However, the diversity of heterogeneous agents including pedestrians, bicycles, and vehicles in traffic scenarios presents substantial challenges to this task. Current models do not fully capture the implicit and explicit interaction relationships among these heterogeneous agents and often overlook the significance of extracting implicit correlations from agent features. To address these issues, we introduce a novel model for trajectory prediction: Heterogeneous Graph Structure Contrast Optimization (HGSCO). To accurately capture the interaction relationships among heterogeneous agents, HGSCO constructs semantic graph structures representing implicit relationships and meta-path graph structures representing explicit relationships. Then, HGSCO introduces a cross-view contrastive learning approach, which optimizes the heterogeneous graph structure by maximizing mutual information between the two types of graph structures. The model can provide precise interaction relationships among heterogeneous agents by effectively fusing these two graph representations with a gated fusion method. We utilized video data captured by camera sensors in complex environments with multiple agents to conduct experiments. Our proposed model achieved an 11.5% and 6.7% reduction in Average Displacement Error (ADE) across these datasets, respectively, and a reduction of 15.6% and 8.1% in Final Displacement Error (FDE). The results demonstrate that HGSCO significantly surpasses existing state-of-the-art methods regarding trajectory prediction accuracy.

Pedestrian behavior anticipation is a key challenge in the design of assistive and autonomous driving systems suitable for urban environments. An intelligent system should be able to understand the intentions or underlying motives of pedestrians and to predict their forthcoming actions. To date, only a few public datasets were proposed for the purpose of studying pedestrian behavior prediction in the context of intelligent driving. To this end, we propose a novel large-scale dataset designed for pedestrian intention estimation (PIE). We conducted a large-scale human experiment to establish human reference data for pedestrian intention in traffic scenes. We propose models for estimating pedestrian crossing intention and predicting their future trajectory. Our intention estimation model achieves 79% accuracy and our trajectory prediction algorithm outperforms state-of-the-art by 26% on the proposed dataset. We further show that combining pedestrian intention with observed motion improves trajectory prediction. The dataset and models are available at http://data.nvision2.eecs.yorku.ca/PIE_dataset/.

Autonomy is becoming more alluring in today's world due to its applications in various fields such as defense, transportation and exploration of unknown terrain. Autonomy in transportation will be common in the near future and prediction of trajectory helps in decision making. Trajectory prediction is crucial and significant for efficient navigation. The behavior of vehicles is not typical like most other dynamic objects due to physical factors such as vehicle size, driver behavior, etc. and social factors such as avoidance of obstacles, an inter-vehicle distance of neighboring vehicles, etc. data-driven vehicle-trajectory prediction model using Generative Adversarial Networks (GANs) is proposed which uses the partial history of the vehicles and learns the complex behavior of a vehicle. The proposed scheme feeds the model the sequence of vehicle positions as the coordinates that are obtained from a traffic scene and result in the probabilistic vehicle position based on vehicle velocities, prioritization and the above social and physical factors. The model is evaluated on aerial views of traffic data such as VisDrone data set and the TRAF data set. The proposed model can learn vehicle behaviors such as overcoming, merging, etc. Independent of vehicle type, the proposed model predicts the future path of each vehicle in the scene given the history of the vehicle and the results are measured using the average and final displacement errors.

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.

A predictive model for mobility systems capable of understanding the trajectory a user intends to follow in the environment is proposed. Understanding user intention is paramount for any shared-control navigation strategy between a user and an active robotic agent. Equally important however is being able to go beyond simple sample generation to assign probabilistic meaning to the set of possible future trajectories, so most likely scenarios can be assumed. The framework estimates a distribution over possible intentions, proposing a novel generative model predicated on Normalizing Flows which accounts for past behaviours, as traditionally reported in the literature, but also incorporates visual scene information. As the model permits trajectories to be assigned exact likelihoods, tractable density estimates can be readily exploited to finalize an executable intention. Baseline comparisons with the publicly available and widely used KITTI navigational dataset show significant improvements (up to 11.08%) with respect to traditional metrics such as Average and Final Displacement Errors. A novel metric that stands independent of the number of samples is also proposed as a more fitting comparison for future works.

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.

Pedestrian behavior prediction is essential to enable safe and efficient driving of intelligent vehicles on urban traffic environment. This article presents a novel framework for pedestrian trajectory prediction, which integrates Dynamic Bayesian network and Sequence-to-Sequence model through an adaptive online weighting method. Dynamic Bayesian network utilizes environmental features and kinematic information to infer the pedestrian's motion intentions through probabilistic reasoning. Sequence-to-Sequence model views trajectory predictions as sequence generation tasks, in which the future trajectories are generated relying on the observed trajectories. A real-world pedestrian motion dataset is employed for model validations and it is also enlarged through data augmentation techniques to enable training of data-driven approaches. We compare our model with several typical baselines methods and results show that our model outperforms those baselines. The average error and the final destination error with one-second prediction are 0.04m and 0.10m in crossing scenarios, and 0.06m and 0.17m in stopping scenarios, respectively. The study expects to provide guidelines for the decision-making of intelligent vehicles in order to protect vulnerable road users.

Understanding crowd motion dynamics and forecasting the future pedestrian trajectories are critical to various applications, e.g. autonomous driving and surveillance system. This task is challenging because when pedestrians plan the future paths in real crowd scenes, they will distinguish the priorities of following their predetermined destinations and responding to the motion behaviors of neighboring pedestrians. However, most of the existing methods ignore the problem of intention-interaction trade-off. In this paper, we tackle this problem by a hierarchical network, which achieves dynamically reasoning predetermined destinations and future trajectories. A novel graph structure called Intention-Interaction Graph (IIG) is designed to jointly model the self intentions and social interactions. To aggregate information in IIG, Interaction Gated Graph Attention Networks (IGGAN) consisting of a gate mechanism and an attention mechanism is proposed, thus achieving reasoning the influence degree of neighboring pedestrians and destinations. Experimental results on multiple widely used pedestrian trajectory prediction datasets, including two datasets in ETH and three datasets in UCY, demonstrate the effectiveness of the proposed model.

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.

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.