Deep Learning Methods for Vessel Trajectory Prediction based on Recurrent Neural Networks

Data-driven methods open up unprecedented possibilities for maritime surveillance using Automatic Identification System (AIS) data. In this work, we explore deep learning strategies using historical AIS observations to address the problem of predicting future vessel trajectories with a prediction horizon of several hours. We propose novel sequence-to-sequence vessel trajectory prediction models based on encoder-decoder recurrent neural networks (RNNs) that are trained on historical trajectory data to predict future trajectory samples given previous observations. The proposed architecture combines Long Short-Term Memory (LSTM) RNNs for sequence modeling to encode the observed data and generate future predictions with different intermediate aggregation layers to capture space-time dependencies in sequential data. Experimental results on vessel trajectories from an AIS dataset made freely available by the Danish Maritime Authority show the effectiveness of deep-learning methods for trajectory prediction based on sequence-to-sequence neural networks, which achieve better performance than baseline approaches based on linear regression or on the Multi-Layer Perceptron (MLP) architecture. The comparative evaluation of results shows: i) the superiority of attention pooling over static pooling for the specific application, and ii) the remarkable performance improvement that can be obtained with labeled trajectories, i.e., when predictions are conditioned on a low-level context representation encoded from the sequence of past observations, as well as on additional inputs (e.g., port of departure or arrival) about the vessel's high-level intention, which may be available from AIS.Comment: Accepted for publications in IEEE Transactions on Aerospace and Electronic Systems, 17 pages, 9 figure

COVID-19 Impact on Global Maritime Mobility

To prevent the outbreak of the Coronavirus disease (COVID-19), many countries around the world went into lockdown and imposed unprecedented containment measures. These restrictions progressively produced changes to social behavior and global mobility patterns, evidently disrupting social and economic activities. Here, using maritime traffic data collected via a global network of AIS receivers, we analyze the effects that the COVID-19 pandemic and containment measures had on the shipping industry, which accounts alone for more than 80% of the world trade. We rely on multiple data-driven maritime mobility indexes to quantitatively assess ship mobility in a given unit of time. The mobility analysis here presented has a worldwide extent and is based on the computation of: CNM of all ships reporting their position and navigational status via AIS, number of active and idle ships, and fleet average speed. To highlight significant changes in shipping routes and operational patterns, we also compute and compare global and local density maps. We compare 2020 mobility levels to those of previous years assuming that an unchanged growth rate would have been achieved, if not for COVID-19. Following the outbreak, we find an unprecedented drop in maritime mobility, across all categories of commercial shipping. With few exceptions, a generally reduced activity is observable from March to June, when the most severe restrictions were in force. We quantify a variation of mobility between -5.62% and -13.77% for container ships, between +2.28% and -3.32% for dry bulk, between -0.22% and -9.27% for wet bulk, and between -19.57% and -42.77% for passenger traffic. This study is unprecedented for the uniqueness and completeness of the employed dataset, which comprises a trillion AIS messages broadcast worldwide by 50000 ships, a figure that closely parallels the documented size of the world merchant fleet

Bayesian Approaches to Tracking, Sensor Fusion and Intent Prediction

This thesis presents work on the development of model-based Bayesian approaches to object tracking and intent prediction. Successful navigation/positioning applications rely fundamentally on the choice of appropriate dynamic model and the design of effective tracking algorithms capable of maximising the use of the structure of the dynamic system and the information from sensors. While the tracking problem with frequent and accurate position data has been well studied, we push back the frontiers of current technology where an object can undergo fast manoeuvres and position fixes are limited. On the other hand, intent prediction techniques which extract higher level information such as the intended destination of a moving object can be designed, given the ability to perform successful tracking. Such techniques can play important roles in various application areas, including traffic monitoring, intelligent human computer interaction systems and autonomous route planning. In the first part of this thesis Bayesian tracking methods are designed based on a standard fix-rate setting in which the dynamic system is formulated into a Markovian state space form. We show that the combination of an intrinsic coordinate dynamic model and sensors in the object's body frame leads to novel state space models according to which efficient proposal kernels can be designed and implemented by the sequential Monte Carlo (SMC) methods. Also, sequential Markov chain Monte Carlo schemes are considered for the first time to tackle the sequential batch inference problems due to the presence of infrequent position data. Performance evaluation on both synthetic and real-world data shows that the proposed algorithms are superior to simpler particle filters, implying that they can be favourable alternatives to tracking problems with inertial sensors. The modelling assumption that leads to Markovian state space models can be restrictive for real-world systems as it stipulates that the state sequence has to be synchronised with the observations. In the second major part of this thesis we relax this assumption and work with a more natural class of models, termed variable rate models. We generalise the existing variable rate intrinsic model to incorporate acceleration, speed, distance and position data and introduce new variable rate particle filtering methods tailored to the derived model to accommodate multi-sensor multi-rate tracking scenarios. The proposed algorithms can achieve substantial improvements in terms of tracking accuracy and robustness over a bootstrap variable rate particle filter. Moreover, full Bayesian inference schemes for the learning of both the hidden state and system parameters are presented, with numerical results illustrating their effectiveness. The last part of the thesis is about designing efficient intent prediction algorithms within a Bayesian framework. A pseudo-observation based approach to the incorporation of destination knowledge is introduced, making the mathematics of the dynamical model and the observation process consistent with the Markov state process. Based on the new interpretation, two algorithms are proposed to sequentially estimate the probability of all possible endpoints. Whilst the synthetic maritime surveillance data demonstrate that the proposed methods can achieve comparable prediction performance with reduced computational cost in comparison to the existing bridging distribution based methods, the results on an extensive freehand pointing database, which contains 95 three-dimensional pointing trajectories, show that the new algorithms can outperform other state-of-the-art techniques. Some sensitivity tests are also performed, confirming the good robustness of the introduced methods against model mismatches

A GPU-based real time trigger for rare kaon decays at NA62

Abstract This thesis reports a study for a new real-time trigger for the NA62 experiment based on Graphical Processing Units (GPUs). The NA62 experiment was devised to study with unprecedented precision the ultra-rare decay K+ → π+ ν anti-ν, a process mediated by Flavour-Changing Neutral Currents (FCNC) whose exceptional theoretical cleanliness provides a unique probe to test the Standard Model. The use of a high-rate kaon beam will result in an event rate of about 15 MHz, so high that it will be impossible to store data on disk without an efficient selection. The experiment therefore devised three trigger levels, allowing to reduce the data rate fed to the readout PC farm down to ∼10 kHz. For this thesis I developed an online trigger algorithm that uses data fed by the RICH (Ring Imaging CHerenkov counter) detector in real-time to allow a rejection of the dominant background K+ → π+ π 0 based on kinematical constraints. As a starting point for the development of this algorithm, I verified the feasibility of such a trigger through Montecarlo simulations. I measured the reconstruction resolution, achieved by the RICH detector alone, of the kinematical variables used for the event selection. After that, I analysed the background rejection power and the signal efficiency of several kinematical constraints, and I designed an actual trigger algorithm. The necessity of running the algorithm in real-time, with a maximum latency of 1 ms per event, drove the choice of exploiting the parallel computing power of GPUs. A parallelized algorithm was therefore developed, that can fit up to 4 Cherenkov rings per event. Moreover, a large number of events are processed concurrently. No parallelized and seedless multi-ring fitting algorithm existed before. The developed algorithm consists of a pattern recognition stage, to assign the hits to up to 4 ring candidates, and of a robust single-ring fit routine. The program was tested on GPUs, and its performance and execution latency proved to be compatible with the requirements. This work proves that alternative trigger designs are possible for the NA62 experiment, and represents a starting point for the introduction of flexible GPU-based real-time triggers in High Energy Physics. Sommario La mia tesi costituisce uno studio per un algoritmo di trigger in tempo reale basato su GPU (Graphical Processing Units) per l’esperimento NA62. NA62 è un esperimento progettato per misurare con precisione il decadimento ultra raro K+ → π+ ν anti-ν, un canale mediato da correnti neutre flavour-changing estremamente sensibile all’eventuale presenza di nuova fisica. L’elevato rate di eventi rivelati, dell’ordine di 15 MHz, non permetterà una archiviazione su disco dei dati non moderata da severi criteri di selezione. Sono perciò necessari dei livelli di trigger che consentano di ridurre il rate di eventi salvati fino a circa una decina di kHz. L’algoritmo sviluppato si basa sull’uso del rivelatore RICH (Ring Imaging CHerenkov counter). Le informazioni primitive inviate dal RICH vengono valutate in tempo reale, per produrre una decisione di trigger basata prevalentemente su considerazioni di cinematica. In una prima fase ho verificato, tramite simulazione Montecarlo, la fattibilità e significatività di tale progetto. Ho dapprima misurato la risoluzione sulla ricostruzione di alcune quantità cinematiche ricavate utilizzando unicamente il rivelatore RICH, poiché per un trigger di primo livello in tempo reale non sarà possibile mettere in relazione dati forniti da rivelatori diversi. Ho studiato poi fino a che livello fosse possibile separare il segnale dal fondo, misurando l’efficienza di reiezione e l’accettanza per il segnale al variare di alcuni parametri di selezione. Data la necessità di eseguire il programma in tempo reale, con una latenza massima di 1 ms per evento, si è deciso di sfruttare il potere computazionale parallelo proprio delle GPU (processori grafici ad elevato parallelismo). E’ stato quindi sviluppato un algoritmo in grado di eseguire simultaneamente non solo le istruzioni relative ad eventi diversi, ma anche i fit di fino a 4 anelli Cherenkov diversi appartenenti allo stesso evento. Nessun algoritmo parallelo e seedless di questo tipo esisteva in letteratura. L’algoritmo implementato è composto di due parti: una iniziale di riconoscimento di pattern, che estrae il numero di anelli presenti nella matrice ed identifica gli hit appartenenti a ciascuno di essi, ed una di fit dei singoli cerchi. Il programma è stato testato su GPU, ed efficienza e tempi di esecuzione risultano compatibili con le richieste. Questo lavoro apre la possibilità di implementare trigger alternativi e flessibili per NA62 e rappresenta un primo esempio prototipale dell’uso di GPU in tempo reale

A GPU-based real time trigger for rare kaon decays at NA62

Abstract This thesis reports a study for a new real-time trigger for the NA62 experiment based on Graphical Processing Units (GPUs). The NA62 experiment was devised to study with unprecedented precision the ultra-rare decay K+ → π+ ν anti-ν, a process mediated by Flavour-Changing Neutral Currents (FCNC) whose exceptional theoretical cleanliness provides a unique probe to test the Standard Model. The use of a high-rate kaon beam will result in an event rate of about 15 MHz, so high that it will be impossible to store data on disk without an efficient selection. The experiment therefore devised three trigger levels, allowing to reduce the data rate fed to the readout PC farm down to ∼10 kHz. For this thesis I developed an online trigger algorithm that uses data fed by the RICH (Ring Imaging CHerenkov counter) detector in real-time to allow a rejection of the dominant background K+ → π+ π 0 based on kinematical constraints. As a starting point for the development of this algorithm, I verified the feasibility of such a trigger through Montecarlo simulations. I measured the reconstruction resolution, achieved by the RICH detector alone, of the kinematical variables used for the event selection. After that, I analysed the background rejection power and the signal efficiency of several kinematical constraints, and I designed an actual trigger algorithm. The necessity of running the algorithm in real-time, with a maximum latency of 1 ms per event, drove the choice of exploiting the parallel computing power of GPUs. A parallelized algorithm was therefore developed, that can fit up to 4 Cherenkov rings per event. Moreover, a large number of events are processed concurrently. No parallelized and seedless multi-ring fitting algorithm existed before. The developed algorithm consists of a pattern recognition stage, to assign the hits to up to 4 ring candidates, and of a robust single-ring fit routine. The program was tested on GPUs, and its performance and execution latency proved to be compatible with the requirements. This work proves that alternative trigger designs are possible for the NA62 experiment, and represents a starting point for the introduction of flexible GPU-based real-time triggers in High Energy Physics. Sommario La mia tesi costituisce uno studio per un algoritmo di trigger in tempo reale basato su GPU (Graphical Processing Units) per l’esperimento NA62. NA62 è un esperimento progettato per misurare con precisione il decadimento ultra raro K+ → π+ ν anti-ν, un canale mediato da correnti neutre flavour-changing estremamente sensibile all’eventuale presenza di nuova fisica. L’elevato rate di eventi rivelati, dell’ordine di 15 MHz, non permetterà una archiviazione su disco dei dati non moderata da severi criteri di selezione. Sono perciò necessari dei livelli di trigger che consentano di ridurre il rate di eventi salvati fino a circa una decina di kHz. L’algoritmo sviluppato si basa sull’uso del rivelatore RICH (Ring Imaging CHerenkov counter). Le informazioni primitive inviate dal RICH vengono valutate in tempo reale, per produrre una decisione di trigger basata prevalentemente su considerazioni di cinematica. In una prima fase ho verificato, tramite simulazione Montecarlo, la fattibilità e significatività di tale progetto. Ho dapprima misurato la risoluzione sulla ricostruzione di alcune quantità cinematiche ricavate utilizzando unicamente il rivelatore RICH, poiché per un trigger di primo livello in tempo reale non sarà possibile mettere in relazione dati forniti da rivelatori diversi. Ho studiato poi fino a che livello fosse possibile separare il segnale dal fondo, misurando l’efficienza di reiezione e l’accettanza per il segnale al variare di alcuni parametri di selezione. Data la necessità di eseguire il programma in tempo reale, con una latenza massima di 1 ms per evento, si è deciso di sfruttare il potere computazionale parallelo proprio delle GPU (processori grafici ad elevato parallelismo). E’ stato quindi sviluppato un algoritmo in grado di eseguire simultaneamente non solo le istruzioni relative ad eventi diversi, ma anche i fit di fino a 4 anelli Cherenkov diversi appartenenti allo stesso evento. Nessun algoritmo parallelo e seedless di questo tipo esisteva in letteratura. L’algoritmo implementato è composto di due parti: una iniziale di riconoscimento di pattern, che estrae il numero di anelli presenti nella matrice ed identifica gli hit appartenenti a ciascuno di essi, ed una di fit dei singoli cerchi. Il programma è stato testato su GPU, ed efficienza e tempi di esecuzione risultano compatibili con le richieste. Questo lavoro apre la possibilità di implementare trigger alternativi e flessibili per NA62 e rappresenta un primo esempio prototipale dell’uso di GPU in tempo reale

Fluctuating hydrodynamics model for homogeneous and heterogeneous vapor bubble nucleation

At the molecular scale, even in conditions of thermodynamic equilibrium, the fluids do not exhibit a deterministic behavior. Going down below the micrometer scale, the effects of thermal fluctuations play a dominant role in the dynamics of the system, calling for a suitable description of thermal fluctuations. These models not only play an important role in physics of fluids, but a deep understanding of these phenomena is necessary for the progress of some of the latest nanotechnology. For instance the modeling of thermal fluctuations is crucial in the design of flow micro-devices, in the study of biological systems, such as lipid membranes, in the theory of Brownian engines and in the development of artificial molecular motor prototypes. Another problem with a huge technological impact is the phenomenon of nucleation – the precursor of the phase transition in metastable systems – in this context related to bubble formation in liquid-vapor phase transition. Vapor bubbles form in liquids by two main mechanisms: boiling, by increasing the tempe- rature over the boiling threshold, and cavitation, by reducing the pressure below the vapor pressure threshold. The liquid can be held in these metastable states (overheating and tensile conditions, respectively) for a long time without forming bubbles. Bubble nucleation is indeed an activated process, requiring a significant amount of energy to overcome the free energy barrier and bring the liquid from the metastable conditions to the thermodynami- cally stable state where vapor is observed. Depending on the thermodynamic conditions, the nucleation time may be exceedingly long, the so-called "rare- event" issue. Nowadays molecular dynamics is the unique tool to investigate such thermally activated processes. However, its computational cost limits its application to small systems (less than few tenth of nanometers) and to very short times, preventing the study of hydrodynamic interactions. The latter effects are crucial to understand the cavitation phenomenon in its entirety, starting from the vapor embryos nucleation up to the macroscopic motion. In this thesis a continuum diffuse interface model of the two-phase fluid has been embedded with thermal fluctuations in the context of the so-called Fluctuating Hydrodynamics (FH) and has been exploited to address cavitation. This model provides a set of partial stochastic differential equations, whose deterministic part is represented by the capillary Navier-Stokes equations and reproducing the Einstein-Boltzmann probability distribution for the macroscopic fields. This mesoscale approach enables the description of the liquid-vapor transition in extended systems and the evaluation of bub- ble nucleation rates in different metastable conditions by means of numerical simulations. Such model is expected to have a huge impact on the understanding of the nucleation dynamics since, by reducing the computational cost by orders of magnitude, it allows the unique possibility of investigating systems of realistic dimensions on macroscopic time scales. In addition, after the nucleating phase, the deterministic equations have been used to address the collapse of a cavitation nanobubble near a solid boundary, showing an unprecedented description of interfacial flows that naturally takes into account topology modification and phase changes (both vapor/liquid and vapor/supercritical fluid transformations)