Managing nonuniformities and uncertainties in vehicle-oriented sensor data over next generation networks

Detailed and accurate vehicle-oriented sensor data is considered fundamental for efficient vehicle-to-everything V2X communication applications, especially in the upcoming highly heterogeneous, brisk and agile 5G networking era. Information retrieval, transfer and manipulation in real-time offers a small margin for erratic behavior, regardless of its root cause. This paper presents a method for managing nonuniformities and uncertainties found on datasets, based on an elaborate Matrix Completion technique, with superior performance in three distinct cases of vehicle-related sensor data, collected under real driving conditions. Our approach appears capable of handling sensing and communication irregularities, minimizing at the same time the storage and transmission requirements of Multi-access Edge Computing applications

Traffic signal detection from in-vehicle GPS speed profiles using functional data analysis and machine learning

International audienceThe increasing availability of large-scale Global Positioning System (GPS) data stemming from in-vehicle embedded terminal devices enables the design of methods deriving road network cartographic information from drivers' recorded traces. Some machine learning approaches have been proposed in the past to train automatic road network map inference, and recently this approach has been successfully extended to infer road attributes as well, such as speed limitation or number of lanes. In this paper, we address the problem of detecting traffic signals from a set of vehicle speed profiles, under a classification perspective. Each data instance is a speed versus distance plot depicting over a hundred profiles on a 100-meter-long road span. We proposed three different ways of deriving features: the first one relies on the raw speed measurements; the second one uses image recognition techniques; and the third one is based on functional data analysis. We input them into most commonly used classification algorithms and a comparative analysis demonstrated that a functional description of speed profiles with wavelet transforms seems to outperform the other approaches with most of the tested classifiers. It also highlighted that Random Forests yield an accurate detection of traffic signals, regardless of the chosen feature extraction method, while keeping a remarkably low confusion rate with stop signs

D32.1: Individual Use Cases and Test Scenarios Definition

ecoDriver targets a 20% reduction of CO2 emissions and fuel consumption in road transport by encouraging the adoption of green driving behaviour. Drivers will receive eco-driving recommendations and feedback adapted to them and to their vehicle characteristics. A range of driving profiles, powertrain

Logarithmic Mean Divisia Index Decomposition of CO2 Emissions from Urban Passenger Transport: An Empirical Study of Global Cities from 1960–2001

The urban transport sector has become one of the major contributors to global CO2 emissions. This paper investigates the driving forces of changes in CO2 emissions from the passenger transport sectors in different cities, which is helpful for formulating effective carbon-reduction policies and strategies. The logarithmic mean Divisia index (LMDI) method is used to decompose the CO2 emissions changes into five driving determinants: Urbanization level, motorization level, mode structure, energy intensity, and energy mix. First, the urban transport CO2 emissions between 1960 and 2001 from 46 global cities are calculated. Then, the multiplicative decomposition results for megacities (London, New York, Paris, and Tokyo) are compared with those of other cities. Moreover, additive decomposition analyses of the 4 megacities are conducted to explore the driving forces of changes in CO2 emissions from the passenger transport sectors in these megacities between 1960 and 2001. Based on the decomposition results, some effective carbon-reduction strategies can be formulated for developing cities experiencing rapid urbanization and motorization. The main suggestions are as follows: (i) Rational land use, such as transit-oriented development, is a feasible way to control the trip distance per capita (ii) fuel economy policies and standards formulated when there are oil crisis are effective ways to suppress the increase of CO2 emissions, and these changes should not be abandoned when oil prices fall and (iii) cities with high population densities should focus on the development of public and non-motorized transport. Document type: Articl

Outcomes from elective colorectal cancer surgery during the SARS-CoV-2 pandemic

This study aimed to describe the change in surgical practice and the impact of SARS-CoV-2 on mortality after surgical resection of colorectal cancer during the initial phases of the SARS-CoV-2 pandemic

Road infrastructure influence on the occurrence of control loss of lightweight vehicles in curves : modelisation and validation on test track

L’infrastructure routière joue un rôle très important dans l’occurrence des pertes de contrôle des véhicules légers en virage. Dans le but d’évaluer le niveau de risque associé à une infrastructure, les gestionnaires routiers utilisent actuellement des méthodes reposant sur des études statistiques ou empiriques, sachant que leur validité peut évoluer au cours temps. Le but de ces travaux est donc de compléter ces méthodes par une évaluation physique des tracés routiers basée sur la dynamique des véhicules. Tout d’abord, le problème a été abordé de manière théorique par une écriture de critères issus des modèles de la dynamique des véhicules (modèles point et bicyclette) visant à identifier et quantifier l’impact des paramètres routiers sur l’occurrence des pertes de contrôle. Ensuite, pour représenter avec plus de précision le comportement du conducteur, une modélisation numérique complète de contrôle du système véhicule-infrastructure-conducteur, basée sur un algorithme génétique, a été mise en œuvre. Enfin, ces deux approches ont été validées expérimentalement pour différentes adhérences, rayons de courbure et véhicules sur la piste de référence du LCPC de Nantes et sur routes interurbaines et urbaines. Ces travaux de thèse ont permis d’approfondir la connaissance du rôle de l’infrastructure et de développer deux méthodes (analytique et numérique) d’évaluation des tracés routiers. En perspectives à cette étude, un logiciel basé sur ces méthodes sera développé.Road infrastructure plays a major role in the occurrence of control loss of lightweight vehicles in curves. To evaluate the risk level associated with an infrastructure, road managers are currently using methods founded on statistical or empirical studies, knowing that their reliability can vary over time. The aim of this work is to complete these methods with a physical evaluation of road plans based on vehicle dynamics. First of all, the problem has been dealt on the theoretical way by writing criteria coming from vehicle dynamic models (point and bicycle models) aiming to identify and quantify the influence of road parameters on control loss occurrence. Then, to faithfully take into account the driver behaviour, a complete numerical modelling of the vehicle-infrastructure-driver, resting on a genetic algorithm, has been introduced. Finally, these two approaches has been validated for various grip, curvature radius and vehicles on the LCPC test track in Nantes and on interurban and urban roads. These thesis works has permitted to improve the knowledge of road infrastructure influence and to develop two road plan evaluation methods (analytical and numerical). As a prospect of this study, a software based on these two methods will be developed

Influence de l'infrastructure routière sur l'occurrence des pertes de contrôle de véhicules légers en virage : Modélisation et validation sur site expérimental

Road infrastructure plays a major role in the occurrence of control loss of passengers cars in curves. To evaluate the risk level associated to an infrastructure, road managers currently use methods founded on statistical or empirical studies, knowing that their reliability can vary over time. The aim of this work is to complete these methods with a physical evaluation of road plans based on vehicle dynamics. First of all, the problem has been dealt in a theoretical way by writing criteria coming from vehicle dynamic models (point and bicycle models) aiming to identify and quantify the influence of road parameters on control loss occurrence. Then, to faithfully take into account the driver behavior, a complete numerical modeling of the vehicle-infrastructure-driver, relying on a genetic algorithm, has been introduced. Finally, these two approaches have been validated for various grip, curvature radius and vehicles on the LCPC test track in Nantes and on interurban and urban roads. These thesis works have permitted to improve the knowledge of road infrastructure influence and to develop two road plan evaluation methods (analytical and numerical). As a prospect of this study, a software for road manager usage will be developed.L'infrastructure routière joue un rôle très important dans l'occurrence des pertes de contrôle des véhicules légers en virage. Dans le but d'évaluer le niveau de risque associé à une infrastructure, les gestionnaires routiers utilisent actuellement des méthodes reposant sur des études statistiques ou empiriques, sachant que leur validité peut évoluer au cours du temps. Le but de ces travaux est donc de compléter ces méthodes par une évaluation physique des tracés routiers fondée sur la dynamique des véhicules. Tout d'abord, le problème a été abordé de manière théorique par une écriture de critères issus des modèles de la dynamique des véhicules (modèles point et bicyclette) visant à identifier et quantifier l'impact des paramètres routiers sur l'occurrence des pertes de contrôle. Ensuite, pour représenter avec plus de précision le comportement du conducteur, une modélisation numérique complète de contrôle du système véhicule-infrastructure-conducteur, fondée sur un algorithme génétique, a été mise en oeuvre. Enfin, ces deux approches ont été validées expérimentalement pour différentes adhérences, rayons de courbure et véhicules sur la piste de référence du LCPC de Nantes et sur routes interurbaines et urbaines. Ces travaux de thèse ont permis d'approfondir la connaissance du rôle de l'infrastructure et de développer deux méthodes (analytique et numérique) d'évaluation des tracés routiers. En perspective à cette étude, un logiciel destiné aux gestionnaires sera développé

Experimental evaluation of curves design rules by analyzing vehicle\infrastructure interactions

Roads geometrical design rest partly on relations between the allowed speed of use, the curvature of the plotting and the banking. These relations have sometimes old justifications as in France where the documents used date from 1994. Work presented here comes in the line off a checking approach of these calculation means. In this direction, a preliminary trial run was carried out with a highly instrumented test vehicle rolling in a turn. The road wetting state influence was evaluated for dry and streaming levels. Comparison between the existing relations 'speed-curvature' and the experiments show that the rules include safety coefficients to take into account the variety of the road usages and the model simplicity. Thus, the users safety is not always assured for the using rules can be broken, if appearing badly founded. So, experiments can help to determine a signage more adapted to the encountered situations, and thus, more respected. Later studies will make it possible to extend this confrontation to various bankings, intermediate wetting states and to a panel of vehicles. These researches will finally contribute to the qualification of road safety offer in relation to the various categories of vehicles which it has to accomodate

Reducing driver's behavioural uncertainties using an interdisciplinary approach: Convergence of Quantified Self, Automated Vehicles, Internet Of Things and Artificial Intelligence.

Growing research progress in Internet of Things (IoT), automated/connected cars, Artificial Intelligence and person’s data acquisition (Quantified Self) will help to reduce behavioral uncertainties in transport and unequivocally influence future transport landscapes. This vision paper argues that by capitalizing advances in data collection and methodologies from emerging research disciplines, we could make the driver amenable to a knowable and monitorable entity, which will improve road safety. We present an interdisciplinary framework, inspired by the Safe system, to extract knowledge from the large amount of available data during driving. The limitation of our approach is discussed