2007 Road Friction Estimation

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Road Friction Estimation

IVSS Project Report

Authors: M. Andersson, F. Bruzelius, J. Casselgren, M. Gfvert, M. Hjort, J. Hultn,F. Hbring, M. Klomp, G. Olsson, M. Sjdahl, J. Svendenius, S. Woxneryd, B. Wlivaara

Contact person: G. Olsson, Saab Automobile AB

Reference number: 2004:17750

Publication date: 2007-06-08


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The IVSS Programme

The IVSS programme was set up to stimulate research and development for the road safety of

the future. The end result will probably be new, smart technologies and new IT systems thatwill help reduce the number of traffic-related fatalities and serious injuries.

IVSS projects shall meet the following three criteria: road safety, economic growth andcommercially marketable technical systems.

Three interacting components - for better safety, growth and competitiveness:

The human being

Preventive solutions based on the vehicles most important component.

The road

Intelligent systems designed to increase security for all road users.

The vehicle

Active safety through pro-active technology.

Injury prevention

Crash avoidance

Business growth

on a global market

Product excellence Premium requirements Cost

IVSS


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Contents

1 Background ........................................................................................................................ 5

1.1 General problem description ......................................................................................5

1.2 Previous work............................................................................................................. 6

2 Objectives and Project Scope............................................................................................. 7

3 Force-Sensor Based Friction Estimation............................................................................ 8

3.1 Approach ....................................................................................................................8

3.2 Results ........................................................................................................................ 9

3.3 Conclusions ..............................................................................................................10

4 Model-Based Friction Estimation ....................................................................................10

4.1 Approach ..................................................................................................................10

4.2 Results ...................................................................................................................... 11

4.3 Conclusions ..............................................................................................................11

5 Preview-Based Friction Estimation.................................................................................. 11

5.1 Approach ..................................................................................................................12

5.2 Results ...................................................................................................................... 12

5.3 Conclusions ..............................................................................................................13

6 Experimental Validation .................................................................................................. 14

6.1 Specific tests............................................................................................................. 14

6.2 Public road tests .......................................................................................................157 Conclusions and Recommendations................................................................................. 16

7.1 Future work ..............................................................................................................16

8 Acknowledgements ..........................................................................................................17

9 Publications ...................................................................................................................... 17

9.1 Public........................................................................................................................17

9.2 Internal .....................................................................................................................17

9.3 Patents ...................................................................................................................... 18

10 References .................................................................................................................... 18


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1 Background

Many traffic-safety related investigations prove a correlation between the road condition andthe number of accidents. In, for example, [1] it has been shown that the risk for accidentsdramatically increases at slippery surfaces. An internal study at Volvo Cars claims that 15 %of all accidents occur owing to low tire-to-road friction. Monitoring of the road conditions is,among vehicle manufacturers, seen as an increasingly important element to support trafficsafety. Knowing the friction value of tire-road interaction it is possible to improve the trafficsafety in several ways, where some examples are:

1. Enhancement of active safety systems of the vehicle. This includes improved performance of systems such as anti-lock braking system (ABS) and electronic stability control(ESC), but also enables new functions such as adaptive followingdistance in ACC and automatic speed adaptation.

2. Driver warning systems based on the internal friction estimation or broadcasted fromother vehicles and/or traffic information centers.

3. Enhanced road maintenance by communication of estimated friction value to the roadauthorities.

4. Enable the possibilities to; for example, decide speed limits on intelligent roads withvariable speed on actual road friction condition.

Much industrial and academic research is concentrated on friction estimation, but still nosatisfactory working method has been presented. It is of strategic and competitive reasons agreat benefit for a company to be able to show a well working road friction estimator.

1.1 General problem description

The tire-to-road friction value, , can be described as the maximum horizontal forcenormalized by the vertical force that can be produced between each tire of the vehicle and theroad. As such, the friction imposes a limitation on the force that can be produced by steering,braking, and throttling and is of critical importance to the ability to stabilize the vehicle alonga desired path. This limitation can be illustrated by the friction circle, where the radius of the

circle represents the -value, see Figure 1.

During acceleration or braking a force is produced along the vertical axis, during corneringalong the horizontal axis, and at combined braking/acceleration and cornering along adirection between the axes. At normal driving the frictional force is not fully utilized and thedeveloped tire force will be somewhere in the interior of the circle. When a force is producedby a tire, a relative motion arises between the tire structure and the road. This relative motionis referred to as tire slip. The relation between the tire slip and the resulting tire force dependson many factors and contains information about the available friction. A typical example of aslip-force relation is shown in Figure 2.

Presence of slip or force is referred to as excitation of the tire-road friction contact. When thetire is exposed to excitation with high utilization, beyond the point corresponding to themaximum available friction force, the tire is sliding and the resulting tire force directlycorresponds to the friction coefficient. For lower utilization, the available friction may beextracted from a model of the tire behavior which is a much more difficult task. Withoutexcitation, the friction characteristics cannot be measured directly.


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Figure 1 Friction circle showing available

frictional force in any sliding direction of the tire.

Figure 2 Example of a slip-force curve. The

inclination at the origin (red line) is referred to as

the braking/cornering stiffness. The road friction is

the maximum relative force.

In general, methods to estimate tire-road friction can be classified into two groups:

1.1.1 Direct methodsDirect methods require excitation and are based on accurate information on the force andmotion in the tire-road contact in combination with a model of the friction influence.

1.1.2 Indirect methods

Indirect methods are typically based on algorithms that as a first step classify the road surfacebased on some type of measurement. The identified road surface is then mapped to a frictionvalue using a look-up table or database. Indirect methods require that all relevant types ofsurfaces are known in advance and stored in the database, and that enough information isavailable from sensors to distinguish between them. A potential advantage with indirectmethods is that friction may be detected on spots ahead of the vehicle and that estimates may

be produced without excitation.

1.2 Previous work

The importance of on-vehicle road friction estimation is generally observed and there is alarge collection of previous attempts to develop methods reported in patents and scientificarticles worldwide, see [h]. The few systems that have found their way to production vehiclesare direct methods characterized by either low accuracy and slow convergence, which makesthem more of a season detection system, or only functional at very high or full frictionutilization like ABS or ESC intervention. Estimation functions aimed at higher availabilityand accuracy have been proposed that are based on a variety of methods, but no solutionexists up to date.

1.2.1 Direct methods

A typical approach is to extract information from wheel-speed measurements at braking orthrottling excitation, or from chassis motion sensors (rate gyro, accelerometers) at corneringexcitation. This has the advantage of low cost since these sensors are already available on thevehicle. These methods may be enhanced by additional sensors such as force sensors in thewheel hubs or steering rack. Common problems with suggested direct methods are lowaccuracy of measured signals, expensive additional sensors, or insufficient algorithms toderive the friction value from measurements.


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One semi-direct approach that has attracted some attention is to directly estimate the tirestiffness, i.e. the inclination of the slip-force at the origin as shown in Figure 2. This value isthen used to distinguish between different road conditions indirectly. However, the tirestiffness depends on many factors and a generic relation to the friction coefficient is likelyhard to obtain.

Tire companies are currently pushing technology for sensors embedded in the tires. These canbe force sensors embedded in the carcass, or optical sensors that measure the tire deformationin the road contact. These sensors may potentially be used in both direct and indirect methods.For example, by measuring horizontal and vertical stress in the tire tread in the sliding parts ofcontact patch, the friction can be derived. This approach still has problems with sensoraccuracy as well as many problems concerning power supply and communication whenmounting a sensor in the tire.

1.2.2 Indirect methods

Some suggested indirect methods are based on surface classification from the noisecharacteristics of the wheel-speed sensor signals. Another approach is to use optical sensors todetect surface properties correlated with friction. Some attempts have been made to classify

the road surface by the sound of the tire-road contact using acoustic sensors, but there are problems in separating information from the surrounding noise. Typical problems withindirect methods are immature or expensive sensor technology, low signal-to-noise ratio ofmeasured entities, or insufficient correlation of measured entities with the friction value.

2 Objectives and Project Scope

The objectives of the project have been to investigate systems and algorithms for online in-vehicle tire to road-surface friction estimation. The focus of the project has been to investigatethe achievable performance of the friction estimation for the chosen technology.

The overall project objectives can be summarized as follows.

To investigate different technologies for online in-vehicle tire to road frictionestimation. Select promising methods and technologies and develop corresponding algorithms. Evaluate selected concepts with respect to accuracy, sensitivity, response time, and

availability using demonstrators in field studies. Propose one or a combination of technologies for further development within the

vehicle industry.

The project has involved wide range of partners from the vehicle industry as well as frominstitutes and universities:

Saab Automobile AB (SAAB), Project coordinator Volvo Technology AB (Volvo)

Volvo Car Corporation (VCC) Haldex Brake Products AB (Haldex) Lule Technical University (LTU) Swedish Road and Transport Research Institute (VTI)

The work has been organized into three subprojects, dealing with different technologies.

1. Force sensor based friction estimation using cornering excitation ( SAAB)2. Model based friction estimation using accelerator excitation (VCC and Haldex)3. Preview, optical sensing (Volvo and LTU)


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Figure 3 Coverage of the three subprojects

0 0.05 0.1 0.15 0.20

20

40

60

Self-aligningTorque,

M

Z

[Nm]

Slip Angle, [rad]

0 0.05 0.1 0.15 0.20

2000

4000

6000

LateralForce,

FY

N]FY

M Z

Figure 4 Self-aligning Torque and Lateral Force

versus tire Slip Angle

Subproject 1 and 2 are based on direct methods that use forces generated at the contact between the tire and the road toestimate the available friction.Subproject 1 has focused on the lateralforces generated at cornering whilesubproject 2 has focused on

longitudinal forces generated atvehicle acceleration. Subproject 3 isbased on an indirect method that usesoptical sensors to classify the roadsurface. The coverage of the differentmethods is illustrated in Figure 3.

VTI has been performingmeasurements of tire characteristicsfor development of a tire model usedin the project and referencemeasurements at test drives with the

subprojects demonstrators.

3 Force-Sensor Based Friction Estimation

Subproject 1 deals with the problem ofestimating the road friction when there is anexcitation in the lateral direction. Theestimation is partly based on measuring theself aligning torque of the front tire. When atire is subjected to lateral forces, anadditional torque around the tires verticalaxis is generated; the so-called self-aligningtorque. This self-aligning torque has the property that it saturates much earlier thanthe lateral force as can be seen from Figure4. The relation between the self-aligningtorque and the tire force discussed in [2] and[3] is used to estimate the frictioncoefficient. The objective of this sub-project

was to evaluate which force-sensing concepts could be employed to measure the self-aligningtorque and how well this would correlate to the tire to road friction value. The secondobjective was to develop and evaluate a lateral force based algorithm for road frictionestimation, utilizing the selected force sensing concept

3.1 ApproachThe test vehicle in Project 1 is a Saab 9-3 2.0t equipped with a dSPACE MicroAutoBox forrapid prototyping as shown in Figure 6. Apart from the standard ESC sensors, Figure 5 showsthree different force sensor concepts, used to estimate the self-aligning torque, which wereevaluated:

1. Steering wheel torque sensor + hydraulic power steering with pressure sensors2. Strain gauges (left and right tie rod)3. Wheel force transducers


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0 1 2 3 4 5 6 7 8 9 100

0.2

0.4

0.6

0.8

1Road Friction Estimate (mue)

Time [s]

mue_min

mue_max

mue

Asphalt

0 1 2 3 4 5 6 7 8 9 100

0.1

0.2

0.3

0.4

0.5Absolute Value of the Lateral Acceleration [g]

Time [s]

abs(Ay)

Asphalt

Figure 7 Results from a slalom manoeuvre across snow, asphalt

and ice at 40 km/h. The black curve indicates when the vehicle is

driven on asphalt.

The least complex and costly solution is the steering gear sensor solution. Improvedperformance at a higher cost is expected from the two other solutions. Principally a sensorclose to the wheel centre should be ideal from performance point of view, since it is close tothe tire to road contact patch. For the other two concepts the forces has to be transmittedthrough joints and other components containing friction and inertia, potentially distorting theforce information

The lateral forced based algorithm studied in this project is based on self-aligning torquemeasurements, as previously mentioned.

3 3

2 2

1 1

1

3 3

2 2

1 1

1

Figure 5 Sensor concepts evaluated for

measuring the self-aligning torque

Yaw rate

Steering angle

Lateral acc.

Wheel speeds

HPS Oil pressure

Tie rod forces

Steering wheel torque

Longitudinal acc.MABX

EBCM signals

Yaw rate

Steering angle

Lateral acc.

Wheel speeds

HPS Oil pressure

Tie rod forces

Steering wheel torque

Longitudinal acc.MABX

EBCM signals

Figure 6 Experimental setup for the self-aligning

torque based road friction estimation.

Furthermore, a parameter identification algorithm based on the response to lateral jerk (therate of change of the lateral acceleration) is used to generate a continuous estimate of thefriction. The self-aligning torque algorithm will, when active, correct this continuous estimateif necessary. This algorithm is described in [4] and its application in [5].

As shown, the test vehicle was also equipped with a longitudinal accelerometer, but is notused for the algorithms that are within the scope of this project.

3.2 Results

The sensor evaluation was performed on dry as well as slippery surfaces. The steering gearsensor concept showed good correlation with the wheel force transducers which were used as

reference. This concept producesa robust signal, however, withless sensitivity to detect lowforces. The tie-rod sensor concepton the other hand is sensitive tolow forces but showed lessrobustness with the tested prototype. The steering gearsensor was selected for thefurther algorithm evaluation.

Final validation tests of thealgorithms were performed inArjeplog in March 2007. InFigure 7 the results from one ofthe tests on well defined surfacesare presented. Since the self-aligning torque based road

friction estimation requires lateral forces, the test is a slalom maneuver at constant speed


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across three different types of surfaces, snow, asphalt and ice at 40 km/h. The black curveindicates when the vehicle is on asphalt, i.e. to the left of the black curve there is snow and tothe right there is ice. These and other results indicate that the sensitivity of using the steeringgear as a force sensing device requires a lateral acceleration of around 0.3g before a reliableestimate of the friction is obtained from this algorithm. This means that asphalt is detected ataround 30% utilization (relative to the available friction), snow between 60-80%. In order to

detect ice, a utilization of nearly 100% is required.Naturally, in cases where the vehicle is driven with lateral acceleration which is less than thesensitivity of the algorithm, the estimate can only be based on knowledge from the latestexcitation.

3.3 Conclusions

By using few additional sensors, the steering gear was proven to be reliable as a force sensingdevice from which the self-aligning torque could be estimated. In vehicles equipped withelectric assisted steering the steering forces can be evaluated without additional sensors. Thealgorithm employed could from this steering force information successfully estimate the roadfriction when the vehicle is cornering. Since cornering situations are essential for many active

safety systems, such as ESC, their performance can be improved if knowing the road frictionin situations where this algorithm is active.

Future work should focus on improving the sensitivity of the force sensing devices in order toimprove the sensitivity of the friction estimate on very slippery surfaces, such ice.

4 Model-Based Friction Estimation

The aim of this project is to estimate the tire to road friction for different types of tires as wellas for different road conditions using only existing signals on a modern car using methodsbased on physical models. In this first phase, the scope is limited to operation at throttlingexcitation on a two wheel driven car at close to straight-line driving.

4.1 Approach

The approach used is to design the estimator with extensive use of physical models. The mainbenefit with algorithms based on physical models is that the necessary calibration effort canbe kept small. Another benefit is that physical models are good tools to interpret and extractinformation from the sensor signals that are used in the algorithms.

The model describing the behavior of the tire to road surface is a central item of the methodand the well-known brush-model was used for this purpose. From the two parameters denotedas the tire stiffness and the coefficient of friction, the relation, visualized as the shape of thecurve shown in Figure 2, is defined. An advantage gained by using this model is that theresulting estimator can be used to detect the tire stiffness as well as the friction coefficient.The tire stiffness is also a parameter of great importance for several active chassis functions.In addition to the tire model, a number of models describing various aspects of the vehiclewere used. For example, a so called bicycle model for the planar chassis motion, a drivelinemodel for torque estimation, and a dynamic load-transfer model.

Based on the physical models, two alternative algorithms were designed:

1. An algorithm based on storing data points in the slip-force plane. These points areused to compute the tire stiffness and the tire to road friction by optimization.

2. An algorithm based on minimization of an error function over a finite parameter grid.


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These two algorithms were equally promising and it was decided to proceed with both.

The algorithms were first developed and tested off-line using vehicle data collected fromextensive test drives. Then, the algorithms were implemented in prototype code and executedin real-time in a test vehicle. The test setup is shown in Figure 8, below.

Figure 8 The rapid prototyping computer, AutoBox, is connected to the vehicle CAN and listens to the

relevant signals. The AutoBox also runs the estimator algorithms. The AutoBox can communicate with a

laptop for tuning and monitoring purposes.

4.2 Results

The algorithms have been evaluated with data from proving ground tests for different surfacetypes and transitions between surface types for summer, winter, and studded tires. The resultis that both estimators are capable of detecting the tire to road friction with acceptableaccuracy and availability. As for all direct methods, sufficient excitation is required in orderto obtain a useful estimate. With the proposed algorithms, reliable estimation results areachieved instantly at acceleration when utilizing 20-50% of the available friction, depending

on surface type and excitation form (ramp, step etc.). The algorithms can also offer a lowerbound on the friction coefficient of twice the currently utilized friction, also when sufficientexcitation is not present. Both algorithms automatically adapt to different types of tireswithout any need for calibration.

The ability to quickly detect changes in road conditions at low excitation is a desired propertyof the estimators. Preliminary results indicate that the proposed methods can be used to detectchanges in road conditions with very little excitation. More work is required to develop andevaluate this feature further.

4.3 Conclusions

By using methods based on physical modeling, it is possible to estimate the tire to road

friction at relatively low utilization. The algorithms are model based and require little tuning.Currently, the algorithms are limited to straight-line driving, but extension to braking andlight cornering conditions is possible. The goal for this subproject is met and it is desired tocontinue to develop the methods further.

5 Preview-Based Friction Estimation

The aim of Subproject 3 was to investigate technology for characterizing the road surface infront of the vehicle, in order to anticipate the coefficient of friction between tire and road.

Wheel speeds

Estimated engine torque

Etc.


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Figure 9 Basic principle of the Road Eye sensor.

b11 b03 b02

50

100

150

200

250

300

350

400

450

meters

log name

Figure 10 Classification of road surfaces. Same track identified during

three different runs. Yellow indicates snow, red indicates ice, black

indicates dry asphalt, blue indicates wet surface.

5.1 Approach

Two candidate sensors were selected based on the requirements and findings in theintroductory study. Both sensors are based on optical techniques measuring and analyzinginfrared light that is reflected in the road surface ahead of the vehicle.

The basic approach is to use sensors measuring infrared light at different wavelengths

reflected from the road to discriminate between road surfaces that are dry, wet, icy, snowy,etc. From theclassification of the roadsurface, a friction estimateis obtained from a table of predetermined frictioncoefficient values.

The selected sensors wereobtained and investigatedin the laboratory at theLTU as well as in field

tests, mounted in a Volvotruck. The field test setupwas used for validation in

winter conditions on prepared test tracks as well as on ordinary roads.

5.2 Results

Two prototype sensors were selected for further investigation and testing. One sensor, calledRoad Eye, was provided by Sten Lfving, Optical Sensors (see Figure 9). The other

candidate, called Sensice,was provided by SensiceInnovation. In addition, asurface temperature sensor

from Control Products Inc.was integrated in the vehicletest setup. During the initialwork with integration of thesensors in the vehicle testenvironment, the Sensicesensor was not able tofunction properly in this project and was thereforenot further pursued.

The laboratory tests

performed by LTU havebeen reported in [a] and [b].In summary, they showedthat the Road Eye sensor isable to discriminate betweena road surface that is dry, orcovered with ice or with


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Figure 11 Accuracy of the Road Eye based classification

method (below) compared to the friction coefficient measured

by VTI (above). The marks show when the estimation is within

(green circle) and outside (red cross) the lower and upper

friction boundaries for the identified surface.

snow. A road surface with water is more difficult to classify accurately. To compensate forthis limitation, a modification is proposed to equip the sensor with one additional laser diodeilluminating the road surface with a third wavelength.

Figure 10 shows the result of a field test where the truck was run across a prepared road withwell defined sections of snow, dry asphalt and ice. The sensor is able to consistently identifythe surfaces.

It is clear from the field tests thatsurfaces with well defined patches of ice, dry asphalt, andsnow can be identified reasonablywell. The estimated friction canthen be given by a table either asthe mid point or as a range offriction coefficient values. Thefriction table is based on previous published measurement resultsand it is not considering the tires

of the actual vehicle.

Figure 11 shows the results of atest run on an ordinary road, witha friction measurement device inone vehicle (the VTImeasurement vehicle) andfollowed by the Road Eyeequipped truck. The road wasmostly covered with packedsnow, occasionally polished to

ice, with some bare spots of dry or wet asphalt. This should be regarded as a difficult test and

even though measured friction quite frequently is outside the range estimated with Road Eye,it seems that the road parts with slight higher and slightly lower friction numbers areidentified reasonably well.

From several test runs in winter conditions on prepared test tracks, like in Figure 10, and onordinary roads, like in Figure 11, the average absolute error in the friction coefficientestimate, compared to the reference measurement, was found to be 0.08. For the same teststhe hit rate, defined as the percentage of the estimates where the measured reference frictionwas within the friction range obtained from the Road Eye classification, was 60%.

Because of the relatively low confidence in the preview friction estimate, the potential use ofthe Road Eye sensor may be in combination with one of the estimation methods investigatedin the other sub projects.

5.3 Conclusions

The conclusion is that the investigated preview sensor technology can be further developedbut should be combined with other techniques. Suggestions for further work are:

Improve the sensor to better discriminate between water and ice.

Investigate methods and potential for sensor fusion and integration with other frictionestimation techniques.


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measurements on homogenous test surfaces. All three methods are able to distinguish between the different surfaces with acceptable accuracy. One exception is that the opticalmethod misinterprets wet asphalt and underestimates the friction.

Table 1 Accuracy test. Friction values measured on specific surfaces.

Subproject 1 2 3 VTI

Snow 0.35 0.3 0.10-0.15 0.25

Ice 0.16 0.1 0-0.1 0.1

Asphalt 0.9 0.7-1.1 0.75 0.65-0.75

Wet asphalt 0.9 0.7-1.1 0.2-0.3 0.7

Slush 0.32 0.3 0.15-0.20

6.2 Public road tests

General system performance on real roads, with varying road conditions, was evaluated on 7road sections and one snow prepared handling track. Testing on road sections was important,not only because they offered realistic road surface conditions, but also to obtain a natural

distribution of curves, hills, and straight sections. One example is shown in Figure 14. Thefirst part of this test road (95) consisted of dry asphalt and the second part, starting after a leftturn, was covered by packed snow and with a combination of snow, ice and asphalt at the end.

Figure 14 Test road 95/Svanns, one of the road sections used for validation. The picture shows the left

turn where the road surface changes character.

Figure 15 shows the estimated and measured friction values using the different methods. Theoptical method correlates quite well with the reference vehicle. The method of subproject 1only gets sufficient excitation at the left turn at about 1400m, where it immediately identifiesthe correct friction. The methods of subproject 2 identifies the friction well on straightsections, but is disengaged in curve sections, such as at the transition to the snowy section.This test illustrates quite well how the different methods complement each other.


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0 500 1000 1500 2000 25000

0.2

0.4

0.6

0.8

1

1.2

1.4

Distance (m)

Fric

tioncoefficient

Road section 1: Colmis => S vans

VTI

Saab

VTEC

VCC1

VCC2

Figure 15 The measured friction values at test road 95/Svanns for VTI BV14 reference (black),

subproject 1 (red), subproject 2 method 1 (blue) and method 2 (light blue), and subproject 3 (green). Note

that the alignments of the different signals are approximate.

7 Conclusions and Recommendations

The aim of this project was to investigate the possibilities to estimate the tire to road friction.Indirect (pre-view sensor based) and direct (force-slip based) methods have been studied. Theresults from all three subprojects are successful with respect to the project goals and allmethods are promising candidates for industrialization. The initial ideas in all threesubprojects have been developed into functional prototypes that were validated under realconditions.

The direct approaches, subproject 1 and 2, show that the tire to road coefficient of friction can be estimated. The availability of the estimate is sufficient enough to provide usefulinformation to active safety functions. The friction can be estimated if there is enough forceexcitation. This fact limits the availability of the estimates to certain driving situations.

The indirect pre-view approach, subproject 3, shows very promising result regardingavailability of surface classification. Investigations suggest that the robustness of theclassifications can be further enhanced by a modification of the present sensor. For thepreview approach, vehicle integration and cost needs further investigation. This is not the casefor the force based approaches as they take advantage of available resources in the vehicle.

The outcome of the subprojects reveals great potential of the investigated technologies andthe results will be used by the industrial partners as support functions to improve the performance of active safety functions. Improving active safety systems increases thecompetitiveness and will contribute to the business growth of the region.

With the rapid development of active safety systems in road vehicles and the increasedavailability of electronics and software communication systems inside and outside thevehicles, it is clear that road friction estimation systems will have a major impact on trafficsafety in the future. Therefore, further development of these technologies is highly needed.

7.1 Future work

The limited scope of the project implies that there are still several issues to be investigatedand problems to be solved, and many new ideas have also emerged during the work. There areno identified fundamental obstacles with any of the methods and it is desired by all partners tocontinue the research and development and bring the technologies to the next level.For the force based approaches (subproject 1 and 2), the availability needs to be improved sothat estimates are available at a larger set of driving conditions. The estimates need to be


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complemented with confidence information to support active safety systems. To support thisdevelopment there is a need for understanding the use cases and to develop proper validationmethods and procedures.

The optical method requires development steps regarding vehicle integration and cost. Inaddition, extensive analysis of different road surfaces is necessary. Therefore furtherdevelopment of this method is also highly recommended.

From a society point of view, friction estimates that support the road infrastructure is of highinterest. The infrastructure communication field has lately drawn a lot of attention, includingthe IVSS programme, and the possibilities are expanding. Road friction estimation and roadconditioning monitoring in this context set new requirements that need to be investigated.8 Acknowledgements

The project is to a great extent financed by IVSS. This financial support is gratefullyacknowledged. Without IVSS, this project would likely not have been initiated. The IVSSproject has not only resulted in several ways of estimating the tire to road friction, but also afruitful network between several companies and universities

9 Publications

The project has lead to a series of publications of which some of them are public, someinternal and some in the form of patent. The publications are listed below:

9.1 Public

[a]Casselgren, J., M. Sjdahl, M. Sanfridsson, and S. Woxneryd. Classification of roadconditions to improve safety. In 11th International Forum on AdvancedMicrosystems for Automotive Applications, (AMAA), Berlin, Germany, 9-10 May2007.

[b]Casselgren, J., M. Sjdahl, and J. LeBlanc. Angular spectral response from covered

asphalt.Applied Optics, Vol 46, No. 22, 2007[c]Casselgren, J., Licentiate Thesis, to appear September 2007.[d]Svendenius, J. Tire Modeling and Friction Estimation. PhD thesis ISRN

LUTFD2/TFRT1077SE. Department of Automatic Control, Lund University,Sweden, 2007.

[e]Svendenius, J. Validation of the brush model towards VTI-measurement datarecorded at Hllered 2005. Technical Report ISRN LUTFD2/TFRT7616SE.Department of Automatic Control, Lund University, Sweden, 2007.

[f] Svendenius, J. Validation of the brush model towards VTI-measurement datarecorded in Arjeplog 2006. Technical Report ISRN LUTFD2/TFRT7617SE.Department of Automatic Control, Lund University, Sweden, 2007.

9.2 Internal

[g]Hbring F. et. al, Force Sensor Based Friction Estimation, Saab report TICA-07-0009, May 2007.

[h]Bruzelius, F., Hultn, J., Gfvert, M. and Svendenius, J, Model-based road frictionestimation, IVSS-RFE internal report sub project 2, May 2007.

[i] Hjort M., Experimental determination of tire friction properties, IVSS-RFE internalreport, May 2007.


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Postal address: IVSS/Swedish Road Administration, SE781 87 Borlnge, Sweden

Street address: IVSS/Swedish Road Administration, NAVET, Lindholmspiren 5, Gothenburg, Sweden

Phone: +46 (0)771 119 119

[email protected]

www.ivss.se

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2007 Road Friction Estimation

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