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In this paper, we are interested in developing a robust tyre-force estimator for heavy duty vehicles. A yaw-roll model is used. The main on-board sensors are provided through the CAN-bus of the vehicle to which are added low-cost sensors. The method is based on the use of a variable gain sliding mode observer. The approach is validated by comparing the estimation results to those given by the software vehicle dynamics PROSPER.

Dans cet article, une méthode d'estimation robuste des forces d'impact dynamiques de poids lourds est présentée. Un modèle couplé est développé : d'une part un modèle de dynamique du châssis dans le plan de lacet et un modèle de dynamique de l'essieu dans le plan de roulis. Dans la méthode proposée, les capteurs déjà présents dans le véhicule (capteurs de l'ABS et de l'ESP) auxquels seront ajoutés des capteurs à moindre coût. En vue d'optimiser la configuration des capteurs, on introduit un différentiateur robuste exact afin d'estimer les accélérations à partir des vitesses mesurées. Le modèle est ensuite inversé pour reconstituer les forces inconnues. L'approche est validée à l'aide du simulateur de dynamique véhicule PROSPER.

Les poids lourds de par leurs caractéristiques géométriques et physiques sont une source importante d'insécurité routière pour l'ensemble des utilisateurs du réseau routier. Une connaissance des transferts de charges dynamiques dans le véhicule constitue une information utile permettant de limiter les risques d'accident à l'aide de systèmes d'alerte et de contrôle actif embarqués. Dans cet article, une méthode d'estimation robuste des forces d'impact de véhicules poids lourd est présentée. Un modèle couplé est utilisé : un modèle de dynamique du châssis dans le plan de lacet et un modèle de dynamique de l'essieu dans le plan de roulis. Dans la méthode proposée, les capteurs issus du bus CAN du véhicule sont exploités auxquels seront ajoutés des capteurs à faible coût. En vue d'optimiser la configuration de capteurs, on introduit un différentiateur exact afin d'estimer les accélérations à partir des vitesses mesurées. Le modèle est ensuite inversé afin de reconstituer les forces inconnues. L'approche est validée à l'aide du simulateur de dynamique véhicule PROSPER.

Le but du travail présenté ici consiste à mettre au point un système actif d'assistance à la conduite du chauffeur pour éviter le renversement du poids lourd. Un estimateur basé sur l'observateur à modes glissants est développé pour estimer les dynamiques du véhicule. L'accélération latérale limite est alors estimée et la position et la vitesse latérales sont contrôlées afin d'éviter le renversement du véhicule. Quelques résultats de simulations sont donnés pour montrer la robustesse et la qualité de ce dispositif.

Le mode routier est dominant pour le transport de marchandises, avec plus de 75% du fret dans la plupart des pays de l'Union européenne. En outre, la proportion de poids lourds croît dans le trafic routier et atteint souvent 15 à 20%. Par conséquent, pour atteindre les nouveaux objectifs de développement durable et pour les infrastructures, une attention particulière doit être portée pour évaluer et limiter les charges dynamiques des roues et essieux sur les chaussées, pour garantir leur durabilité. La sécurité et l'efficacité des poids lourds peuvent être améliorées par une surveillance continue embarquée des charges de roues, servant à des systèmes anti-renversement ou de stabilisation. Les progrès de la théorie du contrôle fournissent des techniques intéressantes pour le pesage en marche embarqué. L'idée est de développer des systèmes intelligents d'estimation des forces d'impacts des poids lourds sur les chaussées et un contrôle actif pour réduire les effets dynamiques maximaux.

In this work, a new method is developed to estimate the unknown inputs of heavy vehicles. These inputs represent road profile which is very interesting since is used to estimate the vertical forces acting on the wheels. We reconstruct these unknown inputs by using higher order sliding mode observer. First, we estimate speeds and accelerations of heavy vehicle in finite time. Validation process is done using an instrumented heavy vehicle and measured road profile by LPA instrument.

Le but de cet ouvrage est la réalisation d'un estimateur de profil de route basé sur les observateurs non linéaires. Nous avons comparé les résultats de plusieurs méthodes au profil mesuré par l'analyseur de profil en long (APL). Nous avons étudié dans un premier temps, la faisabilité de notre approche sur une remorque instrumentée placée sur un support statique. Nous avons alors procédé à une reconstruction des signaux d'excitation. Afin de développer des observateurs, nous avons réalisé un modèle dynamique du véhicule que nous avons validé par des essais sur piste. Pour estimer les quatre entrées, plusieurs méthodes basées sur l'observateur à modes glissants ont été développées. Dans un premier temps, les paramètres du modèle sont considérés connus. Ensuite, un observateur étendu par modes glissants permettant d'estimer les forces longitudinales a été développé pour contourner le problème de la méconnaissance des paramètres. Nous avons ensuite réalisé un observateur triangulaire afin d'obtenir une convergence en temps fini des états. Ceci permet d'obtenir les quatre traces de roulement du véhicule sur la chaussée. Enfin, une étude comparative entre ces différentes méthodes a été réalisée.

In this paper, an experimental validation of vertical forces estimation is presented. An original method using High Order Sliding Mode Observer is developed. The test bench is a static vehicle excited vertically by hydraulic jack. It's equipped by different sensors in order to measure its dynamics. Real time tests are done and results are presented in this paper.

Cet article traite de la validation expérimentale de la prévision de risque de renversement des poids lourds. Une méthode originale de prévision de risque de renversement est décrite. Elle est basée sur le calcul de l'indicateur de risque de renversement qui est le LTR (rapport de transfert de charge) qui dépend des forces verticales estimées en utilisant les techniques d'observateurs à modes glissants. La validation expérimentale est faite sur un banc d'essai représenté par un véhicule léger à l'arrêt.

Cet article présente une nouvelle méthode pour évaluer et prédire dynamiquement le risque de renversement de poids lourds par une approche fiabiliste. La méthode est basée sur la prise en compte des incertitudes du système. Les paramètres les plus influents sur le risque sont représentés par des variables aléatoires, alors que les variables dynamiques sont estimées par un filtre de Kalman Unscented, dédié aux systèmes non linaires. L'ensemble des variables aléatoires obtenues est utilisé pour déterminer à chaque instant un indice de fiabilité permettant de caractériser la sécurité du poids lourd vis-à-vis d'un éventuel risque de renversement. Cet indice est ensuite prédit pour une meilleure prévention du risque. Enfin, des simulations sont effectuées pour valider l'approche développée.

In this paper, an experimental validation of rollover risk evaluation of heavy vehicle is presented. This work was developed by the LCPC in collaboration with its industrial partners, Renault Trucks, Michelin and Sodit in the framework of French project VIF (Véhicule Lourd Interactif du Future). An original method about rollover risk evaluation is described. It's based on the calculation of the LTR (Load Transfer Ratio) which depends on the estimated vertical forces using sliding mode observers techniques.

Compte tenu des progrès techniques réalisés ces dernières années, il paraît opportun de se poser globalement la question des possibilités optimales d'exploitation des nouvelles technologies d'échanges d'information et de la métrologie du trafic pour les véhicules poids lourds de transport routier de marchandises. L'objectif du travail proposé est de développer un système interactif : Poids lourd/Infrastructure/Centre de contrôle permettant d'évaluer les conditions d'accès du poids lourd à l'infrastructure, en fonction de sa charge, les infos trafic et les conditions météorologiques. L'autorisation ou l'interdiction de l'accès du poids lourd est alors décidée. Des techniques de communications sont utilisées pour assurer la communication entre ces trois entités pour échanger des informations.

A lot of researches about vehicle-road interaction have been performed without taking into account the infrastructure characteristics (radius of curvature, skid resistance, evenness, super elevation, slope...). These signals coming from the road are very important in the vehicle dynamics, because they contribute to generate the contact tire forces of heavy vehicles. In this paper, we show the influence of the road profile in the heavy vehicle modelling and we propose a method to evaluate the vertical forces based on third order sliding mode observer. This technic is very common in the dynamic systems field and the results of researchs are very satisfactory. First, we develop a heavy vehicle model in interaction with road. Some simulation results are given in order to prove the robustness of the proposed method. Then the vertical forces are estimated to calculate the LT R (Load Transfer Ratio) coefficient, which allow us to evaluate the rollover risk of heavy vehicle.

Many researches on vehicle-road interaction are performed without taking into account the response of the road profile (surface). However, these signals providing from road are very important in the vehicle dynamics, since they contribute to generate the contact tire forces of heavy vehicles. We show in this paper, the influence of the road profile in the heavy vehicle modelling. In this work, we propose a method to evaluate these vertical forces based on modification of the super twisting algorithm. This technic is very much used for the dynamic systems and the results of various research are very satisfactory. Design of such observers requires a dynamic model. Then, we develop a heavy vehicle model in interaction with road (this road profile is considered as inputs of the heavy vehicle model). In a validation procedure, we compare simulation results coming from a developed heavy vehicle estimator and responses done with the PROSPER's simulator which has been validated with numerous experiments on proving ground. When the vertical forces are finally estimated, we can calculate the LTR (Load Transfer Ratio) coefficient to evaluate the rollover risk.

Vehicle dynamics are dependent on tire/road contact forces and torques, which are themselves dependent on wheels and vehicle loads. These loads induce normal forces which are very important to calculate road and bridge damage or to assess the LTR (Load Transfer Ratio) used to prevent or forecast rollover of the vehicles. A good evaluation of these loads and normal forces requires an accurate description or modelling of the road profile, which is an input to the heavy vehicle dynamic model. In this paper, we study the influence of the road profile elevation on the wheel and vehicle loads. Several road profiles are measured and applied as an input to the heavy dynamic model developed in LCPC ([7]). Then the corresponding wheel loads are evaluated on each wheel. The estimation is done using sliding mode observers. Simulation results from the PROSPER simulator are presented on the estimation of vertical forces, and discussed to evaluate the robustness of our approach and to assess the impact of the road profile.

Vehicle motion simulation accuracy, such as in accident reconstruction or vehicle controllability analysis on real roads, can be obtained only if valid road profile and tire-road friction models are available. Regarding road profiles, a new method based on Sliding Mode Observers has been developed and is compared to LPA measure.

This paper deals with a new method for estimation of the road profile using partial measurements of vehicle dynamics. This method is based on a robust observer designed with a nominal dynamic model of the vehicle. This model is a linear half-car model whose inputs have been considered as unknown states to be estimated. The estimation accuracy of our observer has been validated experimentally using a trailer equipped with position sensors and accelerometers.

In this poster the authors present an experimental validation of the bicycle simulator developed by two IFSTTAR laboratories: LEPSIS and LPC. In the first part, we focus on improving the dynamics of the bicycle model and simulate the effects of road geometry and surface characteristics., {"references":["Shoman Murad, & Imine Hocine. (2020). Modelling and Simulation of Bicycle Dynamics. Zenodo. http://doi.org/10.5281/zenodo.375203"]}

In this paper the authors present an experimental validation of the bicycle simulator developed by two IFSTTAR laboratories: LEPSIS and LPC. In the first part, we focus on improving the dynamics of the bicycle model and simulate the effects of road geometry and surface characteristics such as radius of curvature, road adhesion and unevenness of road profile. In order to verify the accuracy of the developed model, experimental results are shown. For future work, the authors will study the effect of these characteristics on user behaviour to improve the safety and stability of bicycles, particularly in bad weather conditions.

In this paper, we briefly present a subjective experimental validation of the bicycle simulator. In the first part, we present the different physical features of the simulator developed at The Perceptions, Interactions, Behaviors & Simulations Lab for road and street users (PICS-L). In the second part, we present the results of an experiment with 10 participants in order to verify the liability of the simulator. For future work, the authors will deploy the simulator in behavioral studies and compare the outputs with experimentation in real environment seeking for the behavioral validity of the simulator., {"references":["M. Shoman and H. Imine, \"Modeling and simulation of bicycle dynamics \", In TRA 2020, Transportation Research Arena, TRA, 2020, doi:10.5281/zenodo.3775338."]}

In this paper, we briefly present a subjective experimental validation of the bicycle simulator. In the first part, we present the different physical features of the simulator developed at The Perceptions, Interactions, Behaviors & Simulations Lab for road and street users (PICS-L). In the second part, we present the results of an experiment with 10 participants in order to verify the liability of the simulator. For future work, the authors will deploy the simulator in behavioral studies and compare the outputs with experimentation in real environment seeking for the behavioral validity of the simulator.

In this poster the authors present an experimental validation of the bicycle simulator developed by two IFSTTAR laboratories: LEPSIS and LPC. In the first part, we focus on improving the dynamics of the bicycle model and simulate the effects of road geometry and surface characteristics.

In this paper the authors present an experimental validation of the bicycle simulator developed by two IFSTTAR laboratories: LEPSIS and LPC. In the first part, we focus on improving the dynamics of the bicycle model and simulate the effects of road geometry and surface characteristics such as radius of curvature, road adhesion and unevenness of road profile. In order to verify the accuracy of the developed model, experimental results are shown. For future work, the authors will study the effect of these characteristics on user behaviour to improve the safety and stability of bicycles, particularly in bad weather conditions., The conference was cancelled due to corona virus outbreak., {"references":["Babu, SV, Grechkin, TY, Chihak, B., Ziemer, C., Kearney, JK, Cremer, JF, & Plumert, JM (2010). An immersive virtual peer for studying social influences on child cyclists' road-crossing behavior. IEEE Transactions on Visualization and Computer Graphics, 17 (1), 14-25.","Caro, S., & Bernardi, S. (2015). The role of various sensory cues in self-speed perception: a bicycle riding simulator preliminary study. In DSC 2015-Driving simulation conference.","Nehaoua, L. (2008). Conception et réalisation d'une plateforme mécatronique dédiée à la simulation de conduite des véhicules deux-roues motorisés (Doctoral dissertation, Université d'Evry-Val d'Essonne).","Kwon, D. S., Yang, G. H., Lee, C. W., Shin, J. C., Park, Y., Jung, B., ... & Wohn, K. Y. (2001, May). KAIST interactive bicycle simulator. In Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No. 01CH37164) (Vol. 3, pp. 2313-2318). IEEE.","Ghasemi, N., Imine, H & Simone, A., Lantieri, C., & Vignali, V. (2019). Longitudinal Motion Cueing Effects on Driver Behaviour: A Driving Simulator Study. Advances in Transportation Studies: an international Journal Section B 49.","Moore, J. K., Hubbard, M., Kooijman, J. D. G., & Schwab, A. L. (2009, January). A method for estimating physical properties of a combined bicycle and rider. In ASME 2009 international design engineering technical conferences and computers and information in engineering conference (pp. 2011-2020). American Society of Mechanical Engineers.","Kennedy, R.S., Lane, N.E., Berbaum, K.S., & Lilienthal, M.G. (1993). Simulator Sickness Questionnaire: An enhanced method for quantifying simulator sickness. International Journal of Aviation Psychology, 3(3), 203-220.","Hart, S. G. (1986). NASA Task load Index (TLX). Volume 1.0; Paper and pencil package.","Bulsink, V. E., Doria, A., van de Belt, D., & Koopman, B. (2015). The effect of tyre and rider properties on the stability of a bicycle. Advances in mechanical engineering, 7(12), 1687814015622596."]}

In recent years, emissions into the atmosphere have been brought to the attention of the authorities and some action has been taken to try to solve the problem. One is the application of EU legislation 2008/50/EC, which requires states adhering to this law to constantly monitor air quality and subsequently find solutions to reduce the impact of emissions. The data show that 20% of emissions come from transport, 70% of which come from private vehicles. Sustainable mobility can be a possible solution to reduce pollution and traffic congestion. The promotion of cycling, as part of sustainable mobility, is a required action to achieve the objectives pursued. This research aims to define the quality of infrastructure and accessibility of railway stations to the use of bicycles. The approach used was to define a technical checklist to estimate the criticalities of the structure in a quantitative way. An example is the case study developed in the Emilia-Romagna region (Italy) within the PREPAIR project where 33 railway stations were classified and analyzed. In the end, the checklist was effective in defining the necessary interventions and the required activities and can be used in similar cases during the decision-making processes.

The present doctoral thesis discusses the ways to improve the performance of driving simulator, provide objective measures for the road safety evaluation methodology based on driver’s behavior and response and investigates the drivers' adaptation to the driving assistant systems. The activities are divided into two macro areas; the driving simulation studies and on-road experiments. During the driving simulation experimentation, the classical motion cueing algorithm with logarithmic scale was implemented in the 2DOF motion cueing simulator and the motion cues were found desirable by the participants. In addition, it found out that motion stimuli could change the behaviour of the drivers in terms of depth/distance perception. During the on-road experimentations, The driver gaze behaviour was investigated to find the objective measures on the visibility of the road signs and reaction time of the drivers. The sensor infusion and the vehicle monitoring instruments were found useful for an objective assessment of the pavement condition and the drivers’ performance. In the last chapter of the thesis, the safety assessment during the use of level 1 automated driving “ACC” is discussed with the simulator and on-road experiment. The drivers’ visual behaviour was investigated in both studies with innovative classification method to find the epochs of the distraction of the drivers. The behavioural adaptation to ACC showed that drivers may divert their attention away from the driving task to engage in secondary, non-driving-related tasks.