Bayesian Neural ODEs improve vessel trajectory prediction with better uncertainty estimates.
problem Challenges in predicting vessel trajectories from irregular AIS data.
method Adopted a Gaussian process (GP) kernel-based prior on the vector field evaluated at measurement points, combined with probabilistic multiple shooting for long trajectories.
result Improved accuracy and uncertainty quantification in vessel trajectory predictions.
Pattern ensembling fills in missing or inaccurate trajectory data.
problem Incompleteness, missing information, and inaccuracies in geolocation data.
method Probabilistically ensemble similar trajectory patterns from the vicinity.
result Reconstructs missing or unreliable trajectory segments effectively.
The tremendous growth of positioning technologies and GPS enabled devices has produced huge volumes of tracking data during the recent years. This source of information constitutes a rich input for data analytics processes, either offline (e.g. cluster analysis, hot motion discovery) or online (e.g. short-term forecast…
Predicting illegal fishing on Patagonian Shelf using machine learning.
problem Improving detection of illegal fishing to conserve fish stocks.
method Combining vessel tracking data with oceanographic seascapes and machine learning models.
result Machine learning models predict illegal fishing with 69-96% confidence.
The DEBS Grand Challenge 2018 is set in the context of maritime route prediction. Vessel routes are modeled as streams of Automatic Identification System (AIS) data points selected from real-world tracking data. The challenge requires to correctly estimate the destination ports and arrival times of vessel trips, as ear…
In a world of global trading, maritime safety, security and efficiency are crucial issues. We propose a multi-task deep learning framework for vessel monitoring using Automatic Identification System (AIS) data streams. We combine recurrent neural networks with latent variable modeling and an embedding of AIS messages t…
Neural network predicts vessel motions with high accuracy.
problem Real-time prediction of heave and surge motions for improved performance and safety.
method Developed an LSTM-based machine learning model trained on measured waves and motion data.
result The model predicts vessel motions up to 46.5 seconds into the future with an average accuracy of 90%.
Accurately and precisely characterizing the morphology of small pulmonary structures from Computed Tomography (CT) images, such as airways and vessels, is becoming of great importance for diagnosis of pulmonary diseases. The smaller conducting airways are the major site of increased airflow resistance in chronic obstru…
This study aims to predict vessel stay and delay times at ports to optimize logistics.
problem Uncertainties in maritime logistics, including weather, cargo diversity, and port dynamics, lead to increased costs and inefficiencies.
method Developed predictive analytics to address shortcomings in previous works, using feature analysis and SHAP explanations.
result Predictive analytics can assist in efficient planning and scheduling of port operations, reducing costs and improving logistics.
A novel spatio-temporal graph neural network with a learnable Tweedie head improves vessel traffic flow prediction in sparse maritime data.
problem Accurate vessel traffic flow prediction in sparse maritime data.
method A model-agnostic learnable Tweedie head attached to ST-GNN backbones.
result The proposed head consistently improves RMSE across multiple ST-GNN backbones, especially on non-zero events.
This paper discusses challenges and opportunities in vessel behavior detection using machine and deep learning.
problem Real-time analysis of vessel behaviors is crucial for maritime safety and protection.
method Comparison of classical machine learning and deep learning approaches for vessel event and anomaly detection.
result Novel methods and tools are needed to address challenges in vessel behavior detection.
Modeling vessel speed to balance efficiency and environmental risks in Arctic shipping.
problem Balancing vessel speed with environmental and ecological risks in Arctic shipping.
method Inverse control constrained optimization framework with risk parameters estimated from AIS data.
result Distinct decision-making patterns across vessel types and navigational statuses, with varying sensitivity to ice and whale risks.
Vision impairment due to pathological damage of the retina can largely be prevented through periodic screening using fundus color imaging. However the challenge with large scale screening is the inability to exhaustively detect fine blood vessels crucial to disease diagnosis. In this work we present a computational ima…
We study control systems invariant under a Lie group with application to the problem of nonlinear trajectory planning. A theory of symmetry reduction of exterior differential systems is employed to demonstrate how symmetry reduction and reconstruction is effective in the explicit, exact construction of planned system t…
The retinal vascular condition is a reliable biomarker of several ophthalmologic and cardiovascular diseases, so automatic vessel segmentation may be crucial to diagnose and monitor them. In this paper, we propose a novel method that combines the multiscale analysis provided by the Stationary Wavelet Transform with a m…
The installation process of offshore wind turbines requires the use of expensive jack-up vessels. These vessels regularly report their position via the Automatic Identification System (AIS). This paper introduces a novel approach of applying machine learning to AIS data from jack-up vessels. We apply the new method to …
Improved retinal vessel segmentation with topology preservation trade-off.
problem Retinal vessel segmentation accuracy and topology preservation trade-off.
method Topology preserving term in the loss function and orientation score guided convolutional module.
result Model achieves higher topological accuracy at the expense of lower overlap metrics.
A new method tracks retinal vessels more accurately than existing methods.
problem Tracking retinal vessels accurately in spherical images.
method Computing cusp-free, crossing-preserving geodesics on spherical positions and orientations.
result Crossing-preserving tracking shows clear advantages over non-crossing-preserving tracking.
The worldwide growth of maritime traffic and the development of the Automatic Identification System (AIS) has led to advances in monitoring systems for preventing vessel accidents and detecting illegal activities. In this work, we describe research gaps and challenges in machine learning for vessel behavior change and …
Synthesizing images of the eye fundus is a challenging task that has been previously approached by formulating complex models of the anatomy of the eye. New images can then be generated by sampling a suitable parameter space. In this work, we propose a method that learns to synthesize eye fundus images directly from da…
GeoStat simplifies time series classification with fast, intuitive features.
problem Efficiently classify time series data without high computational costs.
method GeoStat representations based on differential geometric statistics.
result Simple KNN and SVM classifiers achieve state-of-the-art performance.
Existing supervised approaches didn't make use of the low-level features which are actually effective to this task. And another deficiency is that they didn't consider the relation between pixels, which means effective features are not extracted. In this paper, we proposed a novel convolutional neural network which mak…
Dual-edge spatial Jacobian image graph for interpretable diabetic retinopathy grading
problem Automated diabetic retinopathy grading from color fundus photographs
method Dual-edge spatial-Jacobian image graph
result 0.8076 accuracy, 0.8312 quadratic weighted kappa, 0.5915 macro-F1, 0.9330 adjacent-grade accuracy
Paper uses deep imitation learning to predict aircraft trajectories accurately.
problem Inefficient and costly Air Traffic Management system limits predictability.
method Generative Adversarial Imitation Learning framework with trajectory clustering and classification.
result Accurate predictions for entire trajectory stages, pre- and tactical.
Improved trajectory prediction for team sports using sparse outputs.
problem Challenging to train deep learning models for player trajectory prediction.
method Sparse trajectory prediction and constant acceleration interpolation.
result Interpolation improves performance for all tested models.
Retinal vessel segmentation is an essential step for fundus image analysis. With the recent advances of deep learning technologies, many convolutional neural networks have been applied in this field, including the successful U-Net. In this work, we firstly modify the U-Net with functional blocks aiming to pursue higher…
Extends Shifts dataset for MS lesion segmentation and marine vessel power estimation.
problem Distributional shift in training and deployment data for ML models.
method Develops new datasets for high-risk industrial applications.
result Demonstrates robustness and uncertainty estimation in new industrial tasks.
Designing and modifying complex hull forms for optimal vessel performances have been a major challenge for naval architects. In the present study, Principal Component Analysis (PCA) is introduced to compress the geometric representation of a group of existing vessels, and the resulting principal scores are manipulated …
QC methods improve reliability of machine learning-based image segmentation.
problem Inaccuracies in machine learning algorithms limit their clinical applicability.
method Analysis and validation of QC approaches for automatic segmentation.
result Aggregation of uncertainty and Dice prediction methods improved segmentation reliability.
Many AI problems, in robotics and other domains, are goal-directed, essentially seeking a trajectory leading to some goal state. In such problems, the way we choose to represent a trajectory underlies algorithms for trajectory prediction and optimization. Interestingly, most all prior work in imitation and reinforcemen…
KEMP predicts long-term trajectories for autonomous driving using keyframes.
problem Predicting future trajectories of road agents for autonomous driving.
method Keyframe-based hierarchical end-to-end deep learning framework.
result Ranked 1st on Waymo Open Motion Dataset Leaderboard.
WayDCM predicts trajectories considering long-term goals, improving accuracy.
problem Predicting future trajectories of dynamic agents in complex environments.
method WayDCM combines DCM and NN to predict intermediate goals and trajectories, considering long-term goals.
result WayDCM outperforms previous methods on the Waymo Open dataset.
PhysVarMix predicts diverse urban trajectories with physics constraints.
problem Predicting complex urban agent trajectories with multiple plausible scenarios.
method Physics-informed variational mixture model combining learning and physics constraints.
result Superior performance compared to existing methods on benchmark datasets.
We present CoverNet, a new method for multimodal, probabilistic trajectory prediction for urban driving. Previous work has employed a variety of methods, including multimodal regression, occupancy maps, and 1-step stochastic policies. We instead frame the trajectory prediction problem as classification over a diverse s…
Method predicts NBA players' multi-modal movement trajectories.
problem Understanding NBA players' decision-making during games.
method LSTM-based architecture with multi-modal loss function.
result Method outperforms state-of-the-art in predicting realistic trajectories.
End-to-end model predicts multiagent trajectories using game theory and neural nets.
problem Predicting trajectories of interacting agents in complex scenarios.
method Hybrid neural net with game-theoretic reasoning, using implicit layers to map preferences to Nash equilibria.
result Trains an interpretable model that predicts future trajectories and transfers to decision making.
Improved Gaussian Process model for predicting trajectories without independence assumption errors.
problem Incorrect independence assumption in previous work on Gaussian Process uncertainty propagation.
method Proposed a novel piecewise linear approximation to correct the independence assumption in continuous models.
result Corrected the independence assumption in Gaussian Process models for predicting trajectories.
In this paper we propose a new method to predict the final destination of vehicle trips based on their initial partial trajectories. We first review how we obtained clustering of trajectories that describes user behaviour. Then, we explain how we model main traffic flow patterns by a mixture of 2d Gaussian distribution…
Improved motion prediction for self-driving cars using trajectory sets and auxiliary losses.
problem Accurately predicting future vehicle motion for self-driving cars.
method Classification over trajectory sets with an auxiliary loss for off-road predictions and spatial-temporal relationships.
result Significant improvement in motion prediction performance on small datasets using map information.
New method predicts vehicle trajectories using map lane centers.
problem Accurate long-term vehicle trajectory prediction.
method Uses map lane centers to generate goal paths and predict trajectories.
result Model outperforms state-of-the-art approaches for 6-second horizon predictions.
Representing maritime traffic patterns and detecting anomalies from them are key to vessel monitoring and maritime situational awareness. We propose a novel approach -- referred to as GeoTrackNet -- for maritime anomaly detection from AIS data streams. Our model exploits state-of-the-art neural network schemes to learn…
Improved neural network predicts tropical storm trajectories and Bayesian intervals.
problem Accurately predicting the trajectories of tropical storms to prevent damage.
method Developed an improved RNN model with dropout to predict Bayesian intervals.
result Neural network dropout values significantly affect prediction accuracy and intervals.
Trajectory owner prediction is the basis for many applications such as personalized recommendation, urban planning. Although much effort has been put on this topic, the results archived are still not good enough. Existing methods mainly employ RNNs to model trajectories semantically due to the inherent sequential attri…
A new reinforcement learning method reduces action complexity for robust control.
problem Deep reinforcement learning's susceptibility to spurious correlations.
method Minimizing trajectory entropy to encourage simple, predictable actions.
result Trajectory Entropy Reinforcement Learning achieves superior performance and robustness.
Predicts disruptions in ADITYA tokamak 12 ms in advance.
problem Detecting disruptions in small tokamaks like ADITYA.
method Uses LSTM network trained on pre-processed tokamak diagnostics.
result Predicts disruptions 12 ms in advance with low computation cost.
As autonomous vehicles (AVs) need to interact with other road users, it is of importance to comprehensively understand the dynamic traffic environment, especially the future possible trajectories of surrounding vehicles. This paper presents an algorithm for long-horizon trajectory prediction of surrounding vehicles usi…
The vision of automated driving is to increase both road safety and efficiency, while offering passengers a convenient travel experience. This requires that autonomous systems correctly estimate the current traffic scene and its likely evolution. In highway scenarios early recognition of cut-in maneuvers is essential f…
This work tackles uncertainty in multi-agent multi-modal trajectory forecasting.
problem Measuring and ranking uncertainty in multi-agent multi-modal trajectory forecasting.
method Proposes collaborative uncertainty (CU) and a CU-aware regression framework.
result The CU-aware regression framework improves SOTA systems' performances.