ANALISI E MODELLAZIONE DELLE INTERAZIONI VEICOLO-PEDONE PER LO SVILUPPO DI SISTEMI ATTIVI DI ASSISTENZA ALLA GUIDA E DI PROTEZIONE DEI PEDONI

La sicurezza e la mobilità dei pedoni sono requisiti basilari che dovrebbero caratterizzare ogni sistema di trasporto urbano. Tuttavia, le morti degli utenti della strada più vulnerabili costituiscono ancora oggi una componente significativa di tutte le vittime della strada nel Mondo. Nonostante gli innumerevoli sforzi compiuti per l’innovazione tecnologica dei veicoli e il riesame degli spazi urbani, le statistiche sull’incidentalità dimostrano la necessità e l’importanza di sviluppare sempre più affidabili sistemi di protezione in grado di diminuire gli impatti sociali ed economici del sistema di trasporto. Sebbene sul mercato di massa siano stati immessi molti sistemi di frenata automatica di emergenza (o AEB, dall’inglese Automatic Emergency Braking), una misura di sicurezza chiave nei veicoli moderni in grado di evitare o mitigare gli effetti di una collisione, diversi ricercatori hanno individuato una nuova strategia per lo sviluppo efficiente di questi sistemi: migliorare la sicurezza dei pedoni nel traffico urbano richiede sistemi “intelligenti” in grado, non solo di comprendere lo stato attuale dell’interazione veicolo-pedone, ma di anticipare proattivamente il futuro modello di rischio dell’evento. In altre parole, prevedere in anticipo le decisioni degli utenti nella scena di traffico, interpretare i comportamenti dei conducenti e definire accurate metriche di valutazione del rischio sono gli obbiettivi da perseguire per raggiungere nuovi traguardi nell’ambito della mobilità sostenibile. Questo elaborato discute la natura globale del problema della sicurezza dei pedoni e i diversi approcci che sono stati sviluppati dai gruppi di ricerca nel Mondo per affrontarlo. Inoltre, la tesi presenta nel dettaglio lo studio, l’implementazione e l’analisi di un innovativo modello di valutazione del rischio, recentemente oggetto di pubblicazione su rivista internazionale, per l’efficientamento dei sistemi di assistenza alla guida esistenti. Il modello proposto, basato su moderne tecniche di Machine Learning e processi di analisi in linea con la letteratura scientifica più recente, è in grado di predire, fino a tre secondi nel futuro, il livello di rischio atteso negli incontri tra veicolo e pedone sulle strisce pedonali in funzione della rappresentazione attuale della scena di traffico tratta da radar e telecamere esterne al veicolo. Infatti, l’algoritmo prototipato fornisce una previsione sequenziale, su più orizzonti temporali, di indicatori di sicurezza operativi che descrivono in continuo il processo di incontro e permettono di annotare le interazioni conflittuali gravi. L’applicazione è stata ottimizzata attraverso dati di mobilità, acquisiti con un simulatore di guida avanzato ad elevato grado di realismo, su un campione di giovani conducenti. Questi ultimi hanno affrontato diversi conflitti veicolo-pedone su un percorso urbano virtuale pianificato. La conoscenza acquisita dal modello in questo contesto potrà essere sfruttata per facilitare l’adattamento online del sistema a nuove situazioni operative e alle diverse caratteristiche comportamentali degli utenti

Road Pavement Asphalt Concretes for Thin Wearing Layers: A Machine Learning Approach towards Stiffness Modulus and Volumetric Properties Prediction

In this study a novel procedure is presented for an efficient development of predictive models of road pavement asphalt concretes mechanical characteristics and volumetric properties, using shallow artificial neural networks. The problems of properly assessing the actual generalization feature of a model and avoiding the effects induced by a fixed training-test data split are addressed. Since machine learning models require a careful definition of the network hyperparameters, a Bayesian approach is presented to set the optimal model configuration. The case study covered a set of 92 asphalt concrete specimens for thin wearing layers

Double-blind placebo-controlled randomized clinical trial on the efficacy of Aerosal® in the treatment of sub-obstructive adenotonsillar hypertrophy and related diseases

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Relationship between immune response to SARS-CoV2 vaccines and development of breakthrough infection in solid organ transplant recipients: the CONTRAST cohort

Background: SARS-CoV-2 vaccination in solid organ transplant (SOT) is associated with poorer antibody response (AbR) compared to non-SOT recipients. However, its impact on the risk of breakthrough infection (BI) should yet be assessed. Methods: Single-center prospective longitudinal cohort study enrolling adult SOT recipients who received SARS-CoV2 vaccination during 1-year period from February 2021, and followed-up to April 30th 2022. Patients were tested for AbR at multiple timepoints. Primary endpoint was BI (laboratory confirmed SARS-CoV2 infection ≥14 days after 2nd dose). Immunization (positive AbR) was considered an intermediate state between vaccination and BI. Probabilities of being in vaccination, immunization and BI states were obtained for each type of graft and vaccination sequence with multistate survival analysis, then multivariable logistic regression was performed to analyse the risk of BI in AbR levels. Results: 614 SOT (275 kidney, 163 liver, 137 heart, 39 lung) recipients were included. Most patients (84.7%) received three vaccine doses, the first two consisted of BNT162b2 and mRNA-1273 in 73.5% and 26.5% of cases, respectively; while at the third dose mRNA-1273 was administered in 59.8% of patients. Overall, 75.4% of patients reached immunization and 18.4% developed BI. Heart transplant recipients showed lowest probability of immunization (0.418) and highest of BI (0.323), all-mRNA-1273 vaccine-sequence showed higher probability of immunization (0.732) and lowest of BI (0.098). Risk of BI was higher for non-high-level AbR, younger age and shorter time from transplant. Conclusions: SOT patients with non-high-level AbR, shorter time from transplantation, and heart recipients are at highest risk of BI

Search for eccentric black hole coalescences during the third observing run of LIGO and Virgo

Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass M>70 M⊙) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities 0<e≤0.3 at 0.33 Gpc−3 yr−1 at 90\% confidence level

Ultralight vector dark matter search using data from the KAGRA O3GK run

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for U(1)B−L gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the U(1)B−L gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM

Search for gravitational-lensing signatures in the full third observing run of the LIGO-Virgo network

Gravitational lensing by massive objects along the line of sight to the source causes distortions of gravitational wave-signals; such distortions may reveal information about fundamental physics, cosmology and astrophysics. In this work, we have extended the search for lensing signatures to all binary black hole events from the third observing run of the LIGO--Virgo network. We search for repeated signals from strong lensing by 1) performing targeted searches for subthreshold signals, 2) calculating the degree of overlap amongst the intrinsic parameters and sky location of pairs of signals, 3) comparing the similarities of the spectrograms amongst pairs of signals, and 4) performing dual-signal Bayesian analysis that takes into account selection effects and astrophysical knowledge. We also search for distortions to the gravitational waveform caused by 1) frequency-independent phase shifts in strongly lensed images, and 2) frequency-dependent modulation of the amplitude and phase due to point masses. None of these searches yields significant evidence for lensing. Finally, we use the non-detection of gravitational-wave lensing to constrain the lensing rate based on the latest merger-rate estimates and the fraction of dark matter composed of compact objects

Bituminous Mixtures Experimental Data Modeling Using a Hyperparameters-Optimized Machine Learning Approach

This study introduces a machine learning approach based on Artificial Neural Networks (ANNs) for the prediction of Marshall test results, stiffness modulus and air voids data of different bituminous mixtures for road pavements. A novel approach for an objective and semi-automatic identification of the optimal ANN’s structure, defined by the so-called hyperparameters, has been introduced and discussed. Mechanical and volumetric data were obtained by conducting laboratory tests on 320 Marshall specimens, and the results were used to train the neural network. The k-fold Cross Validation method has been used for partitioning the available data set, to obtain an unbiased evaluation of the model predictive error. The ANN’s hyperparameters have been optimized using the Bayesian optimization, that overcame efficiently the more costly trial-and-error procedure and automated the hyperparameters tuning. The proposed ANN model is characterized by a Pearson coefficient value of 0.868

Surrogate Safety Measures Prediction at Multiple Timescales in V2P Conflicts Based on Gated Recurrent Unit

Improving pedestrian safety at urban intersections requires intelligent systems that should not only understand the actual vehicle\u2013pedestrian (V2P) interaction state but also proactively anticipate the event\u2019s future severity pattern. This paper presents a Gated Recurrent Unit-based system that aims to predict, up to 3 s ahead in time, the severity level of V2P encounters, depending on the current scene representation drawn from on-board radars\u2019 data. A car-driving simulator experiment has been designed to collect sequential mobility features on a cohort of 65 licensed university students who faced different V2P conflicts on a planned urban route. To accurately describe the pedestrian safety condition during the encounter process, a combination of surrogate safety indicators, namely TAdv (Time Advantage) and T2 (Nearness of the Encroachment), are considered for modeling. Due to the nature of these indicators, multiple recurrent neural networks are trained to separately predict T2 continuous values and TAdv categories. Afterwards, their predictions are exploited to label serious conflict interactions. As a comparison, an additional Gated Recurrent Unit (GRU) neural network is developed to directly predict the severity level of inner-city encounters. The latter neural model reaches the best performance on the test set, scoring a recall value of 0.899. Based on selected threshold values, the presented models can be used to label pedestrians near accident events and to enhance existing intelligent driving systems