Predictive estimation of the road-tire friction coefficient

Holzmann, Frédéric; Bellino, M.; Siegwart, Roland; Bubb, Heiner

doi:10.1109/cacsd-cca-isic.2006.4776762

Cited by 25 publications

(16 citation statements)

References 8 publications

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“…e tire-based response can be further divided into two methods of measuring the tire noise response [9] and measuring the tread deformation [10]. Both of these two methods do not get rid of the drawbacks of requiring additional sensors.…”

Section: Review Of Previous Resultsmentioning

confidence: 99%

Interval Recognition Algorithm of the Pavement Surface Condition Based on Lagrange Interpolation Method

Zhang

2020

Mathematical Problems in Engineering

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The accurate recognition of road condition is one of the important factors that influence vehicle safety performance. This paper comes up with an original mathematical method of an interval recognition algorithm of the pavement surface condition based on Lagrange interpolation. The ordinate of the peak point is solved by the Lagrange interpolation method, and the pavement surface condition is deduced by the interval identification algorithm. The simulation results from six typical roads and the varied pavement surface show that besides the cobblestone pavement which is not common in the daily road, the estimation error of the initial tire-road friction coefficient by the Lagrange interpolation method is less than 2%, the pavement surface condition can be identified by interval recognition algorithm quickly and accurately, and the response time is less than 0.2 seconds.

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Section: Review Of Previous Resultsmentioning

confidence: 99%

Interval Recognition Algorithm of the Pavement Surface Condition Based on Lagrange Interpolation Method

Zhang

2020

Mathematical Problems in Engineering

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“…The cause-based method, also known as the experimental method, uses sensors to measure friction-related parameters and attempts to correlate these parameters with the tire–road friction coefficient. The friction-related parameters are to use a sensor to measure anything from hub sound to road surface optical properties, and these sensors are acoustic [ 9 , 10 , 11 ], optical [ 12 , 13 , 14 , 15 , 16 ], and tread sensors [ 17 , 18 , 19 , 20 , 21 , 22 ]. Meanwhile, the effect-based method attempts to simplify the mathematical models to estimate friction [ 23 , 24 ].…”

Section: Related Workmentioning

confidence: 99%

“…Vikki [ 13 ] used non-contact 3D field technology to obtain the macrotexture of the pavement, and the measuring results showed a positive correlation with tire–road friction. Holzmann [ 14 ] used cameras to take pictures of the current environment, extracted different μ-corresponding patterns based on the overall brightness, and matched these patterns with the current environment to derive the friction coefficient and confidence of the road ahead, while the microphone improved the reliability. Sohini [ 15 ] proposed a two-stage method for indirect tire–road friction coefficient estimation by using camera images.…”

Section: Measurement For Friction Coefficientmentioning

confidence: 99%

Estimation for Runway Friction Coefficient Based on Multi-Sensor Information Fusion and Model Correlation

Niu

Zhang

Tian

et al. 2020

Sensors

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Friction is a crucial factor affecting air accident occurrence on landing or taking off. Tire–runway friction directly contributes to aircraft stability on land. Therefore, an accurate friction estimation is a rising issue for all stakeholders. This paper summarizes the existing measurement methods, and a multi-sensor information fusion scheme is proposed to estimate the friction coefficient between the tire and the runway. Acoustic sensors, optical sensors, tread sensors, and other physical sensors form a sensor system that is used to measure friction-related parameters and fuse them through a neural network. So far, many attempts have been made to link the ground friction coefficient with the aircraft braking friction coefficient. The models that have been developed include the International Runway Friction Index (IRFI), Canada Runway Friction Index (CRFI), and other fitting models. Additionally, this paper attempts to correlate the output of the neural network (estimated friction coefficient) with the correlation model to predict the friction coefficient between the tire and the runway when the aircraft brakes. The sensor system proposed in this paper can be regarded as a mobile weather–runway–tire system, which can estimate the friction coefficient by integrating the runway surface conditions and the tire conditions, and fully consider their common effects. The role of the correlation model is to convert the ground friction coefficient to the grade of the aircraft braking friction coefficient and the information is finally reported to the pilots so that they can make better decisions.

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“…be the domain of all possible values of (β, µ max ). 8 Pick a positive number T as the optimization horizon, whose role will soon be clear. What is particular about the problem in study is that the derivative of both unknown variables are either known or measurable.…”

Section: B Observer Designmentioning

confidence: 99%

“…Andersson used optical sensors measuring infrared light at different wavelengths reflected from the road, such that different road surfaces (dry, wet, icy, snow surfaces) can be identified [7]. Holzmann applied cameras to classify road types by analyzing the texture of road images [8]. Similar studies are also shown in [9]- [13].…”

Section: Introductionmentioning

confidence: 99%

Grid Search Based Tire-Road Friction Estimation

et al. 2020

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The tire-road friction coefficient (µ max) is an important input for vehicle dynamics control system and automated driving modules. However, reliable and accurate measurement of this parameter is difficult and costly in mass-produced vehicles and thus estimation is necessary. In this research, an innovative optimization based framework to estimate µ max is proposed. The observation problem is formulated as a non-convex optimization. A novelty of the framework is that the µ max can be accurately estimated in real time together with side slip angle as a by-product without requiring a good initial guess for the non-convex optimization. A key observation is that the time derivative of µ max and side slip angle can be assumed as zero and computed based on measurement, respectively. This allows the observed variables to be updated at a relatively low frequency w.r.t. the solution of the optimization problem. During the interval between each two neighbouring updating time, the observer estimates the µ max and side slip angle by integrating sensor information based on the last update. To find the global optima approximately, a grid search method is implemented for solving non-convex optimization. The estimation results from the proposed observer and a linearization based observer (lbo) are finally compared under various tire-road conditions with simulations and experiments. The results showed that 1) the proposed observer can always guarantee stability in a wide range of vehicle operations while lbo cannot. 2) w.r.t. root mean square of estimation error, the proposed observer performs overall better than lbo in µ max estimation.

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Predictive estimation of the road-tire friction coefficient

Cited by 25 publications

References 8 publications

Interval Recognition Algorithm of the Pavement Surface Condition Based on Lagrange Interpolation Method

Interval Recognition Algorithm of the Pavement Surface Condition Based on Lagrange Interpolation Method

Estimation for Runway Friction Coefficient Based on Multi-Sensor Information Fusion and Model Correlation

Grid Search Based Tire-Road Friction Estimation

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