High-performance trajectory prediction for civil aircraft

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.

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.

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.

Future air traffic management will require a variety of automated decision support tools to provide conflict-free trajectories and their associated error margins. The ability to correctly forecast aircraft trajectories, i.e. trajectory prediction, is the central component of such automated tools, which will enable continued provision of safe and efficient services in increasingly congested skies. Current approaches for trajectory prediction, available in the open literature, make a number of assumptions in order to simplify the mathematical models of aircraft motion. Furthermore, many existing methods perform three-dimensional trajectory prediction, in which information on expected times of arrival at significant points along the intended aircraft route is not considered. This results in inaccurate trajectories not suitable for conflict detection and resolution. This paper presents a novel four-dimensional trajectory prediction scheme that makes full use of data on expected times of arrival. A three dimensional point-mass model for a standard civil aircraft is used to emulate aircraft dynamics, while the aircraft operating mode is characterised through a set of discrete variables. The aircraft performance model used relies on the EUROCONTROL Base of Aircraft Data (BADA) set and the computed trajectory accounts for the effects of wind. Inputs include navigation data and aircraft intent information, which unambiguously define the trajectory to be computed according to the flight plan. In the proposed model, aircraft intent information is summarised in a simple, but effective, set of instructions contained in a Flight Script. Furthermore, two key innovations to trajectory prediction are introduced. Firstly, a novel scheme to emulate the control system used for aircraft lateral guidance is proposed and secondly, on the basis of aircraft intent information, a new procedure to estimate speed is presented. The performance of the enhanced trajectory model proposed is quantified using a detailed operational dataset (real flight data) captured in a European airspace. The results show that, over an extended time-horizon, the enhanced model is more accurate than two representative existing methods, and that it is suitable for reliable trajectory prediction.

The long-term prediction of hypersonic glide vehicle (HGV) trajectory based on radar tracking data still faces challenges. Existing approaches are hard to provide satisfactory performances in long-term HGV trajectory prediction tasks due to insufficient long-range dependency extraction ability and cumulative errors. To address the above issues, a trajectory prediction model physics-informed Transformer (PIT) is proposed in this work. The top$\tau$-mean attention mechanism and the generative decoder are designed in this model to extract the trajectories' long-range dependency and reduce the cumulative errors. Furthermore, the physical knowledge is applied at both the input and output ends of the model, which further improves the model's prediction accuracy even in the case of missing a small amount of radar tracking data. A simulated HGV trajectory dataset is established based on the dynamic equations for the PIT trajectory prediction model. The experimental results show that the PIT performs better than other state-of-the-art deep neural network models. The additional experiments on a small-scale dataset and datasets with missing tracking data further verify the excellent performance and robustness of the PIT in these cases.

Accurate trajectory prediction of vehicles is essential for reliable autonomous driving. To maintain consistent performance as a vehicle driving around different cities, it is crucial to adapt to changing traffic circumstances and achieve lifelong trajectory prediction model. To realize it, catastrophic forgetting is a main problem to be addressed. In this paper, a divergence measurement method based on conditional Kullback-Leibler divergence is proposed first to evaluate spatiotemporal dependency difference among varied driving circumstances. Then based on generative replay, a novel lifelong vehicle trajectory prediction framework is developed. The framework consists of a conditional generation model and a vehicle trajectory prediction model. The conditional generation model is a generative adversarial network conditioned on position configuration of vehicles. After learning and merging trajectory distribution of vehicles across different cities, the generation model replays trajectories with prior samplings as inputs, which alleviates catastrophic forgetting. The vehicle trajectory prediction model is trained by the replayed trajectories and achieves consistent prediction performance on visited cities. A lifelong experiment setup is established on four open datasets including five tasks. Spatiotemporal dependency divergence is calculated for different tasks. Even though these divergence, the proposed framework exhibits lifelong learning ability and achieves consistent performance on all tasks.

As exploitation of low and medium airspace for air traffic management (ATM) is gaining more attention, aerial vehicles’ security issues pose a major challenge to the air–ground-integrated vehicle networks (AGIVNs). Traditional surveillance technology lacks the capacity to support the intensive ATM of the future. Therefore, an advanced automatic-dependent surveillance-broadcast (ADS-B) technique is applied to track and monitor aerial vehicles in a more effective manner. In this article, we propose a grouping-based conflict detection algorithm based on the preprocessed ADS-B data set, and analyze the experimental results and visualize the detected conflicts. Then, in order to further improve flight safety and conflict detection, the trajectories of the aerial vehicles are predicted based on machine learning-based algorithms. The results are fed into the conflict detection algorithm to execute conflict prediction. It was shown that the trajectory prediction model using long short-term memory (LSTM) can achieve better prediction performance, especially when predicting the long-term trajectory of aerial vehicles. The conflict detection results based on the trajectory prediction methods show that the proposed scheme can make it possible to detect whether there would be conflicts within seconds.

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.

In the domain of autonomous driving, trajectory prediction plays a pivotal role in ensuring the safety and reliability of autonomous systems, especially when navigating complex environments. Unfortunately, trajectory prediction suffers from uncertainty problems due to the randomness inherent in the driving environment, but uncertainty quantification in trajectory prediction is not widely addressed, and most studies rely on deep ensembles methods. This study presents a novel uncertainty-aware multimodal trajectory prediction (UAMTP) model that quantifies aleatoric and epistemic uncertainties through a single forward inference. Our approach employs deterministic single forward pass methods, optimizing computational efficiency while retaining robust prediction accuracy. By decomposing trajectory prediction into velocity and yaw components and quantifying uncertainty in both, the UAMTP model generates multimodal predictions that account for environmental randomness and intention ambiguity. Evaluation on datasets collected by CARLA simulator demonstrates that our model not only outperforms Deep Ensembles-based multimodal trajectory prediction method in terms of accuracy such as minFDE and miss rate metrics but also offers enhanced time to react for collision avoidance scenarios. This research marks a step forward in integrating efficient uncertainty quantification into multimodal trajectory prediction tasks within resource-constrained autonomous driving platforms.

The 4D trajectory is a multi-dimensional time series with plentiful spatial-temporal features and has a high degree of complexity and uncertainty. Aiming at these features of aircraft flight trajectory and the problem that it is difficult for existing trajectory prediction methods to extract spatial-temporal features from the trajectory data at the same time, we propose a novel 4D trajectory prediction hybrid architecture based on deep learning, which combined Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). An 1D convolution is used to extract the spatial dimension feature of the trajectory, and LSTM is used to mine the temporal dimension feature of the trajectory. Hence the high-precision prediction of the 4D trajectory is realized based on the sufficient fusion of the above features. We use real Automatic Dependent Surveillance -Broadcast (ADS-B) historical trajectory data for experiments and compare the proposed method with a single LSTM model and BP model on the same data set. The experimental results show that the trajectory prediction accuracy of the CNN-LSTM hybrid model is superior to a single model. The prediction error is reduced by an average of 21.62% compared to the LSTM model and by an average of 52.45% compared to the BP model. It provides a certain reference for the trajectory prediction research and Air Traffic Management decision-making.

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.

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.

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.

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.