Tire/road friction coefficient estimation applied to road safety

International audienceRecent statistics show that a large number of traffic accidents occur due to a loss of control on vehicle by the driver. This is mainly due to a loss of friction between tire and road. Many of these accidents could be avoided by introducing ADAS (Advanced Driver Assistance Systems) based on the detection of loss of tire/road friction. Friction (more specifically the maximum coefficient of friction) which is a parameter of tire/road interaction, mainly depends on the state of the road (dry, wet, snow, ice) and is closely related to the efforts at the tire level. In this paper, we propose, a new method for the estimation of the maximum tire/road friction coefficient, to automatically detect possible state of loss of friction which result in an abrupt change on the road state. This method is based on an iterative quadratic minimization of the error between the developed lateral force and the model of tire/road interaction. Results validate the application of the method

Estimation embarquée des efforts latéraux et de la dérive d'un véhicule : validation expérimentale

National audienceLes principales préoccupations de la sécurité de conduite sont la compréhension et la prévention des situations critiques. Un examen attentif du nombre d'accidents révèle que la perte du contrôle du véhicule est l'une des causes principales des accidents routiers. L'amélioration de la stabilisation du véhicule est possible lorsque ses paramètres dynamiques sont connus. Certains paramètres fondamentaux de la dynamique, tels que les efforts de contact pneumatiques/chaussée, l'angle de dérive et l'adhérence, ne sont pas disponibles sur les véhicules de série ; par conséquence, ces variables doivent être estimées. L'observateur proposé dans cette étude est de type filtre de Kalman, il est basé sur la réponse dynamique d'un véhicule équipé par des capteurs standards. Cet article décrit le procédé d'estimation et présente des évaluations expérimentales. Les résultats expérimentaux acquis avec le véhicule du laboratoire INRETS-MA prouvent l'exactitude et le potentiel de cette approche

Road profile estimation using an adaptive Youla- Kučera parametric observer: comparison to real profilers

International audienceRoad profile acts as a disturbance input to the vehicle dynamics and results in undesirable vibrations affecting the vehicle stability. A precise information of this data is mandatory for a better understanding of the vehicle dynamics behavior and active vehicle control systems design. However, direct measurements of the road profile are not trivial for technical and economical reasons, and thus alternative solutions are needed. This paper develops a novel observer, known as virtual sensor, suitable for real-time estimation of the road profile. The developed approach is carried on a quarter-car model and on measurements of the vehicle body. The road elevation is modeled as a sinusoidal disturbance signal acting on the vehicle system. Since this signal has unknown and time-varying characteristics, the proposed estimation method implements an adaptive control scheme based on the internal model principle and on the use of Youla-Kučera (YK) parametrization technique (also known as Q-parametrization). For performances assessment, estimations are comparatively evaluated with respect to measurements issued from Longitudinal Profile Analyzer (LPA) and Inertial Profiler (IP) instruments during experimental trials. The proposed method is also compared to the approach provided in (Doumiati et al. (2011)), where a stochastic Kalman filter is applied assuming a linear road model. Results show the effectiveness and pertinence of the present observation scheme

Unscented Kalman filter in intelligent vehicles, in Transportation technologies for sustainability

International audienc

Embedded estimation of the tire/road forces and validation in a laboratory vehicle

International audienc

Embedded estimation of vehicle's vertical and lateral tire forces for behavior diagnosis on the road

COMPIEGNE-BU (601592101) / SudocSudocFranceF

Unscented Kalman filter for real-time vehicle lateral tire forces and sideslip angle estimation

International audienc

Virtual Sensors, Application to Vehicle-Tire Road Normal Forces for Road Safety

International audienc

Estimation of vehicle lateral tire-road forces: a comparison between extended and unscented Kalman filtering

International audienceExtensive research has shown that most of road accidents occur as a result of driver errors. A close examination of accident data reveals that losing the vehicle control is responsible for a huge proportion of car accidents. Preventing such kind of accidents using vehicle control systems, requires certain input data concerning vehicle dynamic parameters and vehicle road interaction. Unfortunately, some parameters like tire-road forces and sideslip angle, which have a major impact on vehicle dynamics, are difficult to measure in a car. Therefore, this data must be estimated. Due to the system nonlinearities and unmodeled dynamics, two observers derived from extended and unscented Kalman filtering techniques are proposed and compared. The estimation process method is based on the dynamic response of a vehicle instrumented with cheap, easily-available standard sensors. Performances are tested and compared to real experimental data acquired using the INRETS-MA Laboratory car. Experimental results demonstrate the ability of this approach to provide accurate estimations, and show its practical potential as a low-cost solution for calculating lateral-tire forces and sideslip angle

Design of a new gain-scheduled LPV/Hinf controller for vehicle’s global chassis control

International audienceThis paper investigates new achievements in chassis control. Active Front Steering (AFS) and Direct Yaw Control (DYC) are optimized together to improve -at once- vehicle’s maneuverability, lateral stability and rollover avoidance. The novelty of this work with respect to other works in the field of chassis control is that the controller relies on one single centralized approach, where the additive steering angle provided by the AFS and the differential braking provided by the DYC are generated to control the vehicle yaw rate, side slip angle and roll motion. The optimal H-infinity control technique based on offline Linear Matrix Inequality (LMI) optimal solutions, in the framework of Linear-Parameter- Varying (LPV) systems, is applied to synthesize the controller. A decision making layer instantly monitors two criteria laying on the lateral stability and the rollover. It sends two endogenous weighted parameters, function of the vehicle dynamics, to adapt the controller dynamics and performances according to the driving conditions. The gain scheduled LPV/H-infinity new control strategy is tested and validated on the professional simulator “SCANeR Studio”. Simulations also show the advantage of introducing the roll motion and rollover criteria in the control architecture, comparing to other powerful controllers neglecting these feature