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VEHICULAR 2020, Ninth International Conference on Advances in Vehicular Systems, Technologies and Applications, Porto, PORTUGAL, 18-/10/2020 - 22/10/2020; Researchers are using driving simulators to design and assess the automated driving and driver assistant systems, due to the safe nature of experimentation in the virtual environment. The motion cues and accelerations felt by the drivers are essential for an accurate perception of the events and the response of the drivers. In this paper, the vehicle dynamic model and the Motion Cueing Algorithm used for the simulation is described in detail, then driver's performance and subjective assessments was studied for the braking, chicane and overtaking maneuver in the 3 different gear shifting scenario. The study demonstrates that the presence of the motion cueing feedback in the driving simulation was satisfactory and gave realistic cues for the participants independent of the gear shifting system, however no significant effect was found from the driver's behavior due to different gear shifting system.

VEHICULAR 2020, Ninth International Conference on Advances in Vehicular Systems, Technologies and Applications, Porto, PORTUGAL, 18-/10/2020 - 22/10/2021; In this paper, we present the new features implemented on the bicycle simulator developed by the Perceptions, Interactions, Behaviors and Simulations Lab for road and street users (PICS-L) at Gustave Eiffel University. The added features were deemed necessary to study road-bicycle interactions. We equipped the simulator platform with: three actuators to render the road profile vibrations, an asphalt specimen attached to the rear tire to render the road adhesion, and a new virtual reality environment to render a part of the city of Vanves in France. Simultaneously, we developed a mathematical model with 6 degrees of freedom including the three rotational angles (Yaw, Pitch and Roll) and their influence on vertical, lateral and longitudinal modeling. In order to validate the simulator and the developed model physically and subjectively, we conducted an experiment involving 36 participants who rode the simulator for around 600 meters with full control on the handlebar, pedals and brakes. The improved simulator/mathematical model will be employed to further study bicycle dynamics, cyclist behavior and the interaction with the infrastructure and other road users.

I presented this poster at JTR 2020 conference in Nantes to introduce my research project and show some results.

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.

Driving simulator provides a safe environment to measure driving behaviour by using several sensory cueing systems that enhance the driver’s immersion in the virtual environment. The motion cueing system in driving simulator provides information on the vehicle movements with the use of motion cueing algorithms. The present study aims to investigate the effect of different motion cueing algorithms in a car following and braking task. The algorithms were developed in MATLAB-Simulink and were tested by 20 participants in a driving simulator. The simulator sickness questionnaire and session evaluation questionnaire were used to evaluate the simulator experience. The participants were able to rate the driving simulation experience based on the perceived motion. By investigating driver behaviour, Inter-vehicular distance has changed with motion scenarios. Indicators such as time to reaction, time to collision and effective braking time were examined with statistical test, but no difference was observed with different motion feedback. The study illustrated that different motion cues in driving simulator did not affect the cognitive and motor skills of the participants; however, it influenced the driver perception of the relative distance in the braking scenario.

Accidents involving Heavy Vehicles (HV) have serious consequences for road users,and incidents induce major congestion or damage to the environment or the infrastructure. Statistics show that accidents involving such vehicles are more serious than light vehicles (LV). The mortality rate is twice in an accident involving a heavy vehicle (see table 1.1). While they constitute only 3% vehicles in traffic, HV represent10% of the vehicles involved in fatal accidents (13% of HV-related accidents aredeadly against 6% for LV, and 85% of deaths are not HV users). Several types of truck accidents can occur on the road. The most important risks involving HV are rollover (Roll instability), jackknifing (Yaw instability), lane departure (Transversal instability) and collision (Longitudinal instability). All these accidents are characterized by appropriate risks criteria. These are used in accident analysis and designing appropriate prevention systems (warning systems and active control). In this work, we focus on the criteria of rollover and lane departure, but the developed approaches can be generalized to other criteria and other types of accidents.

The presented low-cost driving simulator designed and built in LEPSiS (Laboratory for road Operations, Perception, Simulators and Simulations) is specialized in the reproduction of the yaw angle. In order to simulate the vehicle behaviour in real time, a vehicle model has been written in Matlab/Simulink. The algorithm has been developed into independent sub-models to allow for easy replacement and upgrades. During the present research, the upgraded sub-models are the ones concerning the powertrain of the vehicle: starting from the driver commands up to the torque on the transmission shaft. The simulated vehicle and the instrumented vehicle owned by the laboratory are both a Peugeot 406. The control of the movements actually reproduced by the simulator is carried out by means of sensors, placed under the driver seat. A triaxial accelerometer, a triaxial gyrometer and a laser distance-meter have been installed. They are controlled by a MicroAutoBox data acquisition system. Results from the model have been validated using a simulator software, Prosper/Callas by Oktal, on which a virtual model of the Peugeot 406 has been created. In order to compare the outputs, the same manoeuvres performed on the driving simulator have been reproduced on Prosper.

ICIEM'14 - Conférence internationale sur l'ingénierie industrielle et productique, BATNA, ALGERIE, 11-/05/2014 - 13/05/2014; Dans ce papier, nous nous sommes basés sur un modèle dynamique articulé nominal d'un véhicule tout terrain ainsi que du contact pneu/chaussée et du plan de la chaussée pour d'écrire le comportement d'un véhicule et sa dynamique par la dynamique de Lagrange.

Dans le but de calculer le système d’équation global du modèle dynamique du véhicule et vu la complexité de ce système, du point de vue du nombre de variables dynamiques et de la non-linéarité du système, nous avons développé dans ce travail les algorithmes de génération automatique du modèle dynamique, en utilisant l’approche de Lagrange basée sur le calcul de l’énergie cinétique et potentielle. Dans cette présentation, nous avons simplifié la structure dynamique du véhicule, tout en garantissant un certain degré de réalisme et d’efficacité de la modélisation, en utilisant les outils de conception robotique. Nous avons utilisé la notion de liaison cinématique (prismatique et rotoide) entre les différents corps et nous avons choisi la structure arborescente pour définir les repères liés aux corps les uns par rapport aux autres jusqu'au repère fixe. Dans cette modélisation, nous avons considéré les roues comme des organes terminaux ou la position du point de contact de chaque roue par rapport au repère fixe est calculée selon le principe des matrices de passage. Nous avons utilisé la formule empirique proposée par Bakker et Pacejka afin de décrire le comportement du pneu en se basant sur les relevés expérimentaux fournis par IFSTTAR (ex LCPC).

During the past two decades, significant progress has been made in the field of higher order sliding mode controllers and observers. These observers/controllers ensure the insensibility of the system with respect to matched smooth uncertainties/perturbations by usage of the absolutely continuous controllers. Moreover, higher order sliding mode-based observers ensure the best possible asymptotic accuracy of the state observation and unknown inputs identification with respect to sampling step and deterministic bounded noises. This explains why several researchers have started to use the new techniques for the vehicle dynamics analysis and design. The objective of this Special Issue of IJVD is to present recent research concerning vehicle dynamics, going from the dynamics observation (acceleration, side slip, force estimation, etc.), parametric identification, risk prediction (rollover, jackknifing, etc.) and vehicle control (lane departure avoidance, steering control, etc.) based on modern variable structure techniques (sliding mode observers, sliding mode observers based identification, sliding mode (SM) control). The specific topics of interest within the scope of this special issue include: SM-based vehicles parameters and forces estimation, SM-based control of automotive driver robot, SM control of motor bicycle, SM control of unicycle, SM control of wheel slip, SM-based observer of heavy vehicle, Vehicle disturbance observation.

In this chapter, an original approach to heavy vehicles rollover risk prediction is presented and validated experimentally. It is based on the calculation of the LTR (Load Transfer Ratio) which depends on the estimated vertical forces using high order sliding mode observers. Previously, a tractor model is developed. The validation tests were carried out on an instrumented truck rolling on the road at various speeds and lane-change manoeuvres. Many scenarios have been experienced : driving straight, curved trajectories, zigzag manoeuvre and brake tests to emphasize the rollover phenomenon and its prediction to set off an alarm for the driver. In this study, the vehicle dynamic parameters (masses, inertias, stiffness..) and the static forces infrastructure characteristics (road profile, radius of curvature, longitudinal and lateral slope, skid resistance) are measured or calculated before the tests.

In the present work, an active steering assistance system for heavy vehicles is developed to prevent lane departure and let the vehicle follow the road’s centre line. The controller is based on the super-twisting algorithm. An estimator based on the sliding mode observer is also developed to estimate the vehicle dynamics. The lateral positions and speeds are then controlled to keep the vehicle in the centre of the lane. The lateral offset and the relative yaw angle are supposed to be measured and the road curvature to be known. Some simulation results are given to show the quality of this steering assistance system.

Une synthèse de mes activités scientifiques et pédagogiques est présentée dans ce document. Elle résume le déroulement de ma carrière de chargé de recherche depuis ma nomination en février 2005 au sein du Laboratoire Central des Ponts et chaussées (LCPC), devenu aujourd'hui IFSTTAR (Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux) depuis sa fusion avec l'INRETS (l'Institut de Recherche sur les Transports et leur Sécurité). En effet, après une thèse de doctorat soutenue en 2003, qui portait sur la dynamique du véhicule et estimation des entrées inconnues issues de la chaussée, et sept années en tant que chargé de recherche, j'ai décidé cette année de franchir le pas et passer mon HDR. Ma motivation est double. D'abord, parce que je me sens aujourd’hui prêt à la vue de mon parcours, comme cela est précisé dans mon rapport d'évaluation de chercheurs établi par le comité d'évaluation des chercheurs du ministère. Ensuite il est important pour la suite de ma carrière d'avoir la reconnaissance de mes pairs, qui est sanctionnée par une Habilitation à Diriger des Recherches. Ceci m'aiderait sans doute à avancer dans ma réflexion scientifique et dans mon processus de maturation intellectuelle. Durant ces dernières années, j'ai développé un axe de recherche, autour des systèmes d'aides à la conduite appliqués, dans un premier temps aux poids lourds et plus récemment aux deux roues motorisés suite à la création de l'UMR, LEPSIS.

The aim of presented work consists in developing an active steering assistance system to avoid the rollover of heavy vehicle. Single body model of heavy vehicle is developed and presented in this paper. An estimator based on the high order sliding mode observer is developed in order to estimate the vehicle dynamics. The lateral acceleration limit is then estimated and lateral position and lateral speed are controlled using twisting algorithm in order to avoid the rollover of the vehicle. In the same time, an identification of some parameters of the model has been done in order to increase the robustness of the method. Some simulation results are given to show the quality of the proposed approach.

This paper presents a motorized two-wheeled vehicle automation project. More precisely, an approach of a motorcycle control starting from its system to establish an autopilot is proposed. There are several objectives: improve the dynamic behaviour of a motorcycle, develop control tools to assist the driver… etc. In this paper, we present the first phase of this project, related to the motorcycle instrumentation by several sensors in order to measure its behaviour and to deduce its attitude in the space. The second phase will consists of the addition of actuators to automate its control units (front and rear brakes, throttle, steering). The third phase is devoted to the control architecture. We also present methodology to measure the position of the gravity centre and estimate the inertia moment of steering system. These two parameters are very important in the dynamic model which is under development.

Vehicles are complex mechanical systems with strong nonlinear characteristics and which can present some uncertainties due to their dynamic parameters such as masses, inertias, suspension springs, tires side slip coefficients, etc. A vehicle is composed of many parts, namely the unsprung mass, the sprung mass, the suspension which makes the link between these two masses and therefore ensures passenger comfort, and also the pneumatic which absorbs the energy coming from the road and ensures contact between the vehicle and the road. In addition to its complexity and the presence of many nonlinearities and uncertainties, the presence of some external perturbations, such as the wind and the road inputs with its own characteristics (radius of curvature, longitudinal and lateral slop, road profile and skid resistance) can cause risks not only to the vehicle but also to passengers and other road users. Many methods have been developed in order to understand the behavior of a vehicle, control it and assist the driver in order to avoid possible lane departures, rollover or jackknifing risks, to ensure a better passenger comfort by means of a suspension control and/or to estimate a safety speed and trajectory. The present book is an attempt to show how the sliding mode based observation, uncertainties identification and parameter estimation may be applied in the control of vehicle dynamics as well as for parameter and perturbations estimation.