FRICTION POTENTIAL AND SAFETY : PREDICTION OF

BRITE EURAM PROJECT BRPR CT97 0461 2 ND INTERNATIONAL COLLOQUIUM ON VEHICLE TYRE ROAD INTERACTION FRICTION POTENTIAL AND SAFETY : PREDICTION OF HANDLING BEHAVIOR FLORENCE, FEBRUARY 23 rd 2001 Title : Correlations between trailer/vehicle braking tests data and testing conditions Authors: Diego Donadio, Diego Speziari - Pirelli Tyres ABSTRACT An analysis of correlations showing the effects of the tyre tread compound on the performance of the tyre in braking has been conducted, focusing on specific parameters and matching their ranking with the results of over 800 trailer tests. In this context, the variation of performances with the imposed testing conditions have been verified. All the presented data have been provided within the context of Vert European research project. Florence February 23 rd 2001 Paper 01.10 pag. 1

INTRODUCTION A huge amount of braking tests on trailers has been performed within the VERT European project. In some cases these tests represented the 100% longitudinal part of a more complete test, including also lateral and combined characterisation of the tire. However, only longitudinal behaviours have been taken in account in this work. Correlations have been traced between the results of the tests and the three subsequent groups of data: - A set of structural lumped parameters of the tire; - A set of laboratory properties of the rubber compound of the tire; - Test conditions In this paper, correlations between the performance indexes and the third group of variables ( testing conditions ) are presented. Moreover, CETE has performed several braking tests on a Xantia, with and without the activation of ABS system: they have been taken in account also. A general scheme of the situation is represented below. Florence February 23 rd 2001 Paper 01.10 pag. 2

AVAILABLE TEST DATA Each one of the tests is characterised by several factors, as follows: - The testing device (depending on the partner who made each test). - The surface nature. The surfaces on which the tests have been carried out are: - Dry asphalt - Wet asphalt - Snow - Ice However, each one of the general categories above includes two of more different particular types of ground surface. The difference is often valuable in the resulting data, since they depend, for example, on the asphalt texture or the snow compactness. - The tire characteristics, particulary: - The tire general size and type (including manufacturer, summer/winter, etc.) - The tire tread depth - For the Tread Compound group, the compound of the tread layer. - The water film thickness misured on the ground. - The speed (generally included between 50 ad 100 Km/h). - The vertical load on the tire. It has been considered as an internal parameter of each test, assuming more than one value; i.e., each test has been conducted with different loads (tipically, three) in order to define also the dependence of the tire behaviour on the normal force. In this work, all the performance indexes are normalized on a 4000N vertical load. Other parameters that have not been used to define and identify the test but, generally, could have an influence are: - Inflating pressure. It is strictly related to the tire type, and has not been considered as test condition variable; although a narrow number of tests have been performed with an inflating pressure slightly different from the reference value. - Air temperature and humidity. These data have been recorded during the tests, but not yet statistically compared with the testing outputs (in some cases of incoherence, they have been considered, but without reaching any satisfying explanations). The next tables show in more detail the combinations of the testing conditions. Florence February 23 rd 2001 Paper 01.10 pag. 3

Activity of each partner Type general types and distribution to partners Florence February 23 rd 2001 Paper 01.10 pag. 4

Tyre Tyre Code Tread Compound code 175 / 65 R 14 Summer 175 S AD 175 / 65 R 14 Winter 175 W --- 185 / 65 R 15 Summer 185 S AA 185 / 65 R 15 Winter 185 W WN (100Hz only) 195 / 65 R 15 Summer 195 S AA 195 / 65 R 15 Winter 195 W --- 225 / 45 R 17 Summer 225 S 99 225 / 45 R 17 Winter 225 W WN (100Hz only) AIPC (smooth) AIPC --- 195 / 65 R15 CO99 99 195 / 65 R15 COAA AA 195 / 65 R15 CO97 97 195 / 65 R15 CO98 98 195 / 65 R15 COAB AB 195 / 65 R15 COAC AC 175 / 65 R14 Studless 175 WL WD 175 / 65 R14 Stud 175 WS WE 195 / 65 R15 Studless 195 WL WF 195 / 65 R15 Stud 195 WS WG Tyres sizes and codes Combined ad Long + Lateral tests performed Florence February 23 rd 2001 Paper 01.10 pag. 5

Distribution of braking tests available (darker green: wet) Synthesis outputs of the tests Total: 834 For each test, following the procedure described in the next paragrafh we obtained a set of n braking force versus longitudinal slippage curves (with n=number of vertical loads imposed); the performance obtained has been condensed in four parameters: - Peak value of braking normalised force (µ MAX ) - The corresponding value of longitudinal slippage (x PEAK ) - The slope of the curve in the linear field (K) - The braking force at locked wheel (SLIP). These are the data used to perform the correlations, crossing them with tire dinamic structural parameters and tread compound laboratory parameters (see subsequent paragraph). The xpeak value does not appear in this report, because it is always strictly inversely related to te slope (K), hence their correlations with other parameters are always quite similar. Florence February 23 rd 2001 Paper 01.10 pag. 6

Synthetic outputs generating procedure The steps followed to generate the results to be analysed, starting from the raw data provided by partners, were: - Putting together (when necessary) and arranging the files in.xls format - Importing them in an Access Database - Running queries in order to fit some fields with standard values related to tyre type and conditions - Fitting all the data with Magic Formulae and obtaining Pacejka coefficients - Checking raw data quality and fitting reliability - Automatically generating the test output values of interest (Curve slope in linear field, maximum value, slip value, maximum/slip ratio, peak slippage value). Once available the Pacejka coefficients for each test, it is possible to calculate the longitudinal force for each condition of longitudinal slippage and vertical load, but the interpolation is reliable only in the range defined by the minimum and maximum load applied during the test. All the sintesis parameters calculated are referred to vertical loads of 3500N and 4000N, included in most ranges. All the correlations have finally been done uniquely referring to 4000N load, that all the indexes are normalized with. Peak, slide, slope values Florence February 23 rd 2001 Paper 01.10 pag. 7

Structural lumped tyre parameters. The picture illustrates a two-dimensions lumped parameters model which is able to represent the tire in-plane behaviour in a frequency range up to about 100Hz. It is modeled as two rigid parts (hub and ring), linked by equivalent springs and damper; the transient nature of contact longitudinal force is given by a 1 st order differential equation, suitable for the range of small slippage values 1. The values of the lumped parameters relative to some of the Pirelli tires tested had been previously identified and then have been included among the correlations data. Actually, the reliability of these values is not fully ensured; this is the reason why their correlations are not often mentioned inside this work. 1 Cfr. Techniques for determining the paramet ers of a two-dimensional tire model for the study of the ride comfort, G. Matrascia, Tire science and technology, 1997, Vol. 25, Number 3 Florence February 23 rd 2001 Paper 01.10 pag. 8

TRAILER TESTS ON WET ASPHALT The major number of tests in this task have been conducted on wet asphalt; the testing condition were very various, because of the change of: - Speed - Water depth - Tread depth generating definitely different results. Consequently, in order to analyse the results, each time a complex multi-variable analysis has been done, based on the optimisation of a linear model taking in account the influences of a set of parameters, which are: - Clas s parameters: - The tyre size (only in the case of the whole data group, see paragraph 2.2.1) - Testing device - Surface type - Numerical parameters: - Tyre tread depth - Speed - Water depth - Studied parameters (structural; compound) Florence February 23 rd 2001 Paper 01.10 pag. 9

The next table resumes the different combinations of tests performed on wet asphalt: Device Detailed Surface Water_Depth NRows CETE Asphalt CETE 1 149 CETE Asphalt CETE 3 148 CETE Asphalt CETE 8 112 DA Asphalt Vizzola 1 19 DA Asphalt Vizzola 3 1 DA Misto Esterno Vizzola 1 1 PIRELLI Asphalt Vizzola 3 22 PIRELLI Asphalt Vizzola 7 24 PIRELLI Asphalt Vizzola 1 51 VTI Asphalt VTI 1 92 VTI Asphalt VTI 3 46 VTI Asphalt VTI 7 59 Total: 724 Velocities: 20, 30, 40, 50, 60, 80, 90, 100 Km/h Tread Depths: 0, 2, 4 mm. The multi-variable model for wet group has been optimised with the following specifications: Object variable: Effect variables: µ MAX ; K (slope); Slide - Speed - Water depth - Surface - Device - Tyre size - Tread depth Results are showed above. - m MAX value - Florence February 23 rd 2001 Paper 01.10 pag. 10

The lower are the blue indexes on the right side, the higher and stronger is the influence of the relative variable on the predicted variable (in this case, µ MAX ). Therefore, the influences of speed, water and tyre tread depths, tyre type, type of surface are strong and clear, as showed in the next diagrams. The device influence is less clear. The warning LostDFs means that the number of possible values of the variable is too high relative to the number of data and other variables entered in the model, making less clear the influence of the same variable. The lower is the range of values (numerical or not) assumed by each variable, the higher is the probability to find a good relation. The next table shows the specific influence of each parameter ( Estimate coloumn), with a numer that must be considered as a multiplication coefficient in the case of numeric variables (speed, depths) and a value associated with each possible value assumed by the variable in the case of category variables (such as Tyre type, device, surface). Here the considered output is the µmax=maximum_force/load. Response: Mu RSquare 0.632882 Observations (or Sum Wgts) 717 Parameter Estimates Term Estimate Std Error t Ratio Prob> t Intercept Biased 1.0717868 0.04566 23.47 <.0001 Speed -0.005887 0.000236-24.95 <.0001 Water Depth -0.018896 0.002232-8.47 <.0001 Tread Depth 0.0360969 0.002415 14.95 <.0001 Tyre[175S-COAC] 0.0079722 0.020241 0.39 0.6938 Tyre[185S-COAC] 0.0441819 0.022008 2.01 0.0451 Tyre[185W-COAC] -0.139854 0.039096-3.58 0.0004 Tyre[195S-COAC] 0.0641043 0.017654 3.63 0.0003 Tyre[195W-COAC] -0.105119 0.019219-5.47 <.0001 Tyre[225S-COAC] 0.0258355 0.017917 1.44 0.1498 Tyre[AIPC-COAC] -0.035639 0.031068-1.15 0.2517 Tyre[CO97-COAC] -0.010502 0.054645-0.19 0.8477 Tyre[CO98-COAC] -0.031339 0.054645-0.57 0.5665 Tyre[CO99-COAC] 0.0653865 0.058643 1.11 0.2652 Tyre[COAA-COAC] 0.0512075 0.058643 0.87 0.3829 Tyre[COAB-COAC] 0.0353291 0.058643 0.60 0.5471 Device[CE-VT] Biased 0.0635392 0.040486 1.57 0.1170 Device[DA -VT] Biased -0.29526 0.064127-4.60 <.0001 Device[PI-VT] Biased -0.155031 0.045334-3.42 0.0007 Surface[BB-ME] Biased -0.144328 0.015109-9.55 <.0001 Surface[DC-ME] Biased -0.328348 0.170536-1.93 0.0546 Surface[ES -ME] Zeroed 0 0?? Surface[F1 -ME] Biased 0.1521303 0.03361 4.53 <.0001 Surface[F3 -ME] Zeroed 0 0?? Florence February 23 rd 2001 Paper 01.10 pag. 11

Speed Water Depth 1.3 1.1 0.9 1 0.7 0.5 0.3 0.1-0 -0.1 10 20 30 40 50 60 70 80 90 100 Speed Leverage Prob>F: <0.0001 Tread Depth 0 1 2 3 4 5 6 7 8 Water Depth Leverage Prob>F: <0.0001 Tyre 1 0 0 1 2 3 4 5 6 7 8 Tread Depth Leverage Prob>F: <0.0001 Device Prob>F: <0.0001 Surface Prob>F: 0.0010 Prob>F: <0.0001 Florence February 23 rd 2001 Paper 01.10 pag. 12

Remarks - Note that tread and water thicknessess in the diagrams shown above are not the few, discrete values really used in the tests, but they appear as an amount of continously changing values. This happens because the showed diagrams actually are of legerage type (Sall, 1990) 2. - The main effect of reduction of peak force value with the increase of water depth and speed and with the reduction of the tire tread depth, is fully noticeable and is in the correct directions. The effect of the tread thickness appears to be stronger than the water depth one. - Tyre different properties effect also is evident; the worse behaviour of winter sizes on wet is acceptable, while more surprising is the collocation of 225/45 R17 Summer tyres among more normal sizes. The same ranking visible in the leverage plot can be retrieved in the table of parameter estimates. - Similar models have been calculated for the subsequent parameters: - K - Slide - Peak/Slide in the subsequent, the most relevant results only will be shown. 2 in a leverage plot, the horizontal position of each point can be shifted with reference to the true value assumed by the sample (for numeric variables), since it is representative of the influence of the single data. The shift is influenced by the other effects in the model. In the case of category variable, the horizontal position is determined by the resulting ranking, and could make visible different groups of data with a very different behaviour. However, the more the region in which the line is traced is narrow, and the slope of this line is high, the more the effect is consistent. For more informations about leverage plots, see JMP3.1 Statistics and Graphics Guide, page 153. Florence February 23 rd 2001 Paper 01.10 pag. 13

- initial slope value - Response: K Summary of Fit RSquare 0.479533 RSquare Adj 0.463692 Root Mean Square Error 47463.24 Mean of Response 104237.8 Observations (or Sum Wgts) 712 Parameter Estimates Term Estimate Std Error t Ratio Prob> t Intercept Biased 209357.25 14359.83 14.58 <.0001 Speed -541.0519 74.40647-7.27 <.0001 Water Depth -4047.631 700.0284-5.78 <.0001 Tread Depth -2300.296 756.3385-3.04 0.0024 Tyre[175S-COAC] -33116.46 6408.437-5.17 <.0001 Tyre[185S-COAC] -11449.55 6928.856-1.65 0.0989 Tyre[185W-COAC] -25297.01 12245.29-2.07 0.0392 Tyre[195S-COAC] 14507.661 5579.396 2.60 0.0095 Tyre[195W-COAC] -58633.62 6068.455-9.66 <.0001 Tyre[225S-COAC] 1730.4298 5658.075 0.31 0.7598 Tyre[AIPC-COAC] -51185.94 9753.614-5.25 <.0001 Tyre[CO97-COAC] 41368.482 18366.98 2.25 0.0246 Tyre[CO98-COAC] 40524.704 18366.98 2.21 0.0277 Tyre[CO99-COAC] 33620.05 18366.98 1.83 0.0676 Tyre[COAA-COAC] 17484.416 18366.98 0.95 0.3415 Tyre[COAB-COAC] 8557.2513 18366.98 0.47 0.6414 Device[CE-VT] Biased -31258.68 12669.54-2.47 0.0139 Device[DA -VT] Biased -66365.82 20123.26-3.30 0.0010 Device[PI-VT] Biased -48847.19 14217.74-3.44 0.0006 Surface[BB-ME] Biased -11077.53 4738.919-2.34 0.0197 Surface[DC-ME] Biased -197618.5 53419.89-3.70 0.0002 Surface[ES-ME] Zeroed 0 0?? Surface[F1-ME] Biased 100476.19 10589.67 9.49 <.0001 Surface[F3-ME] Zeroed 0 0?? Effect Test Source Nparm DF Sum of Squares F Ratio Prob>F Speed 1 1 1.19116e11 52.8757 <.0001 Water Depth 1 1 7.53157e10 33.4326 <.0001 Tread Depth 1 1 2.08377e10 9.2499 0.0024 Tyre 12 12 5.20328e11 19.2478 <.0001 Device 3 1 3902944185 1.7325 0.1885 LostDFs Surface 5 3 2.15586e11 31.8995 <.0001 LostDFs Florence February 23 rd 2001 Paper 01.10 pag. 14

Whole-Model Test 400000 300000 200000 100000 0 0 100000 200000 300000 400000 K Predicted Speed Water Depth 400000 400000 300000 300000 200000 200000 100000 100000 0 0-100000 10 20 30 40 50 60 70 80 90 100 Speed Leverage -100000 0 1 2 3 4 5 6 7 8 Water Depth Leverage Prob>F :<0.0001 Tread Depth Prob>F :<0.0001 Tyre 400000 400000 300000 300000 200000 200000 100000 100000 0 0-100000 0 1 2 3 4 5 6 7 8 Tread Depth Leverage -100000 50000 70000 90000 110000 140000 Tyre Leverage Prob>F :<0.0024 Prob>F :<0.0001 Florence February 23 rd 2001 Paper 01.10 pag. 15

Device Surface 400000 400000 300000 300000 200000 200000 100000 100000 0 0-100000 100000 110000 Device Leverage Prob>F :<0.1885, LostDFs 50000 70000 90000 110000 140000 Surface Leverage Prob>F :<0.0001, LostDFs Note that the most relevant effects on braking behaviour slope at small slippages are the changes in surface and tyre type. Speed and water depth effects are much smaller, but in the correct directions. Particularly, the next plot shows the leverage effect of each one of the surfaces: BB CETE DC VTI ES CETE F1 Vizzola (Pirelli) F3 Vizzola (Pirelli) ME Vizzola (Pirelli) Application of devices on surfaces However, the stiffness is generally the most difficult parameter to correctly identify, since it is strongly affected by possibile errors and precision loss in the global slippage measurement, which can be critical especially for lower values. In addition to that, it must be considered that in most cases each surface has been tested with the respective device, depending on the owner (see table above), and therefore the influence of the surface could actually hide the behaviour of the device. But the effect test and the leverage plots (see previous pages) show a non clear correlation with the variety of device levels. This seems to confirm the real influence of the surface texture. These considerations are done within the multi-variable model, i.e. taking in account the effect of speed and water and tread depth. Otherwise, it should be difficult to get to any conclusions. Florence February 23 rd 2001 Paper 01.10 pag. 16

Response: Slide Summary of Fit RSquare 0.731731 RSquare Adj 0.723601 Root Mean Square Error 397.853 Mean of Response 1950.765 Observations (or Sum Wgts) 715 - slide value - Parameter Estimates Term Estimate Std Error t Ratio Prob> t Intercept Biased 2967.0239 119.7755 24.77 <.0001 Speed -21.58159 0.620457-34.78 <.0001 Water Depth -19.11312 5.881331-3.25 0.0012 Tread Depth 88.410175 6.340159 13.94 <.0001 Tyre[175S-COAC] 154.90307 53.15622 2.91 0.0037 Tyre[185S-COAC] 74.944473 57.71904 1.30 0.1946 Tyre[185W-COAC] -29.12839 102.5362-0.28 0.7764 Tyre[195S-COAC] 132.19862 46.30494 2.85 0.0044 Tyre[195W-COAC] -97.96988 50.48261-1.94 0.0527 Tyre[225S-COAC] 61.548095 46.99607 1.31 0.1908 Tyre[AIPC-COAC] 134.03046 81.51494 1.64 0.1006 Tyre[CO97-COAC] -415.9156 143.3156-2.90 0.0038 Tyre[CO98-COAC] -335.597 143.3156-2.34 0.0195 Tyre[CO99-COAC] 161.5243 153.8 1.05 0.2940 Tyre[COAA-COAC] 64.475466 153.8 0.42 0.6752 Tyre[COAB-COAC] 49.970466 153.8 0.32 0.7454 Device[CE-VT] Biased 91.274653 106.1955 0.86 0.3904 Device[DA -VT] Biased -432.6358 168.1913-2.57 0.0103 Device[PI-VT] Biased -917.1499 118.9122-7.71 <.0001 Surface[BB-ME] Biased -477.4711 39.71254-12.02 <.0001 Surface[DC-ME] Biased -1248.249 447.2639-2.79 0.0054 Surface[ES-ME] Zeroed 0 0?? Surface[F1-ME] Biased 407.19656 88.18809 4.62 <.0001 Surface[F3-ME] Zeroed 0 0?? Effect Test Source Nparm DF Sum of Squares F Ratio Prob>F Speed 1 1 191508772 1209.883 <.0001 Water Depth 1 1 167169 5 10.5612 0.0012 Tread Depth 1 1 30778646 194.4483 <.0001 Tyre 12 12 7452556 3.9235 <.0001 Device 3 1 3001895 18.9649 <.0001 LostDFs Surface 5 3 27118689 57.1087 <.0001 LostDFs Florence February 23 rd 2001 Paper 01.10 pag. 17

Whole-Model Test 4000 3000 2000 1000 0 0 1000 2000 3000 4000 Slide Predicted 4000 Speed 4000 Water Depth 3000 3000 2000 2000 1000 1000 4000 0 10 20 30 40 50 60 70 80 90 100 Speed Leverage Prob>F :<0.0001 Tread Depth 4000 0 0 1 2 3 4 5 6 7 8 Water Depth Leverage Prob>F :<0.0012 Tyre 3000 3000 2000 2000 1000 1000 0 0 0 1 2 3 4 5 6 7 8 Tread Depth Leverage Prob>F :<0.0001 1500 1600 1700 1800 1900 2000 2100 Tyre Leverage Prob>F :<0.0001 Florence February 23 rd 2001 Paper 01.10 pag. 18

4000 Device 4000 Surface 3000 3000 2000 2000 1000 1000 0 0 1800 1900 2000 2100 2200 2300 Device Leverage Prob>F :<0.0001 1600 1800 2000 2200 2400 2600 2800 Surface Leverage Prob>F :<0.0001 Florence February 23 rd 2001 Paper 01.10 pag. 19

- peak/slide ratio- Response: Peak/Slide Summary of Fit Rsquare 0.517958 RSquare Adj 0.503245 Root Mean Square Error 0.361137 Mean of Response 1.848575 Observations (or Sum Wgts) 710 Parameter Estimates Term Estimate Std Error t Ratio Prob> t Intercept Biased 1.3788427 0.109287 12.62 <.0001 Speed 0.0092421 0.000568 16.28 <.0001 Water Depth -0.023599 0.005351-4.41 <.0001 Tread Depth -0.029416 0.005762-5.11 <.0001 Tyre[175S-COAC] -0.079305 0.048829-1.62 0.1048 Tyre[185S-COAC] 0.0302382 0.052721 0.57 0.5665 Tyre[185W-COAC] -0.133475 0.093174-1.43 0.1524 Tyre[195S-COAC] 0.0886843 0.042456 2.09 0.0371 Tyre[195W-COAC] -0.136811 0.046244-2.96 0.0032 Tyre[225S-COAC] 0.0651224 0.043055 1.51 0.1309 Tyre[AIPC-COAC] -0.299896 0.074243-4.04 <.0001 Tyre[CO97-COAC] 0.1582527 0.13975 1.13 0.2579 Tyre[CO98-COAC] 0.0142829 0.13975 0.10 0.9186 Tyre[CO99-COAC] 0.0208995 0.13975 0.15 0.8812 Tyre[COAA-COAC] 0.1005479 0.13975 0.72 0.4721 Tyre[COAB-COAC] 0.0866876 0.13975 0.62 0.5353 Device[CE-VT] Biased 0.1141482 0.096413 1.18 0.2368 Device[DA -VT] Biased 0.1960365 0.153119 1.28 0.2009 Device[PI-VT] Biased 0.9718816 0.108195 8.98 <.0001 Surface[BB-ME] Biased 0.0374943 0.036138 1.04 0.2999 Surface[DC-ME] Biased 1.2780793 0.406466 3.14 0.0017 Surface[ES-ME] Zeroed 0 0?? Surface[F1-ME] Biased -0.473172 0.080609-5.87 <.0001 Surface[F3-ME] Zeroed 0 0?? Effect Test Source Nparm DF Sum of Squares F Ratio Prob>F Speed 1 1 34.556695 264.9645 <.0001 Water Depth 1 1 2.536325 19.4474 <.0001 Tread Depth 1 1 3.399273 26.0640 <.0001 Tyre 12 12 7.850319 5.0160 <.0001 Device 3 1 7.654692 58.6926 <.0001 LostDFs Surface 5 3 4.777456 12.2104 <.0001 LostDFs Florence February 23 rd 2001 Paper 01.10 pag. 20

5.0 Whole-Model Test 4.0 3.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 Peak/Slide Predicted 5.0 Speed 5.0 Water Depth 4.0 4.0 3.0 3.0 2.0 2.0 1.0 5.0 10 20 30 40 50 60 70 80 90 100 Speed Leverage Prob>F :<0.0001 Tread Depth 1.0 5.0 0 1 2 3 4 5 6 7 8 Water Depth Leverage Prob>F :<0.0001 Tyre 4.0 4.0 3.0 3.0 2.0 2.0 1.0 0 1 2 3 4 5 6 7 8 Tread Depth Leverage Prob>F :<0.0001 1.0 1.60 1.70 1.80 1.90 2.00 Tyre Leverage Prob>F :<0.0001 Florence February 23 rd 2001 Paper 01.10 pag. 21

5.0 Device 5.0 Surface 4.0 4.0 3.0 3.0 2.0 2.0 1.0 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 Device Leverage Prob>F :<0.0001, LostDFs 1.0 1.5 1.6 1.7 1.8 1.9 2.0 2.1 Surface Leverage Prob>F :<0.0001, LostDFs Florence February 23 rd 2001 Paper 01.10 pag. 22

It is interesting to observe the general influence of speed on the peak/slide ratio (first plot of previous page). With increasing speed, the ratio tends to increase, emphasising the gain due to the use of an hypothetical ABS system, as explained in the next slide: This consideration is comfirmed by the vehicle tests performed with and without use of ABS system, on dry and wet asphalt (see relative section). Below, the found general effects of wet testing conditions on performance parameters are summarised: Florence February 23 rd 2001 Paper 01.10 pag. 23

TRAILER TESTS ON SNOW The distribution of braking tests available on snow is the following: 175WL 175WS 195WL 195WS 175WL 175WS 195WL 195WS 175WL 175WS 195WL 195WS Device Surface Tread Depth: always 8 mm ALL SIZES : MULTI-VAR MODEL Whole-Model Test 0.45 0.40 0.35 0.30 0.25 0.20 175WL 175WS 195WL 195WS 0.15 0.10.10.15.20.25.30.35.40.45 Mu Predicted R 2 = 0.964 Florence February 23 rd 2001 Paper 01.10 pag. 24

Response: Mu Summary of Fit Rsquare 0.964024 RSquare Adj 0.932545 Root Mean Square Error 0.026733 Mean of Response 0.296366 Observations (or Sum Wgts) 46 Parameter Estimates Term Estimate Std Error t Ratio Prob> t Intercept 0.2805119 0.023612 11.88 <.0001 Tyre[175M-195WS] -0.069443 0.026073-2.66 0.0136 Tyre[175S-195WS] -0.024522 0.026073-0.94 0.3563 Tyre[175W-195WS] 0.1080777 0.026073 4.15 0.0004 Tyre[185W-195WS] 0.1443135 0.026889 5.37 <.0001 Tyre[195S-195WS] -0.109141 0.012448-8.77 <.0001 Tyre[195W-195WS] 0.0863474 0.012448 6.94 <.0001 Tyre[225S-195WS] -0.176735 0.026073-6.78 <.0001 Tyre[225W-195WS] 0.1014947 0.026073 3.89 0.0007 Tyre[CO97-195WS] -0.076723 0.018689-4.11 0.0004 Tyre[CO98-195WS] -0.064583 0.018689-3.46 0.0021 Tyre[CO99-195WS] -0.119187 0.018689-6.38 <.0001 Tyre[COAA-195WS] -0.065641 0.018689-3.51 0.0018 Tyre[COAB-195WS] -0.076827 0.013365-5.75 <.0001 Tyre[COAC-195WS] -0.05381 0.018689-2.88 0.0083 Tyre[175WL-195WS] 0.0884327 0.026073 3.39 0.0024 Tyre[175WS-195WS] 0.0987867 0.026073 3.79 0.0009 Tyre[195WL-195WS] 0.1106337 0.014968 7.39 <.0001 Surf_cod[LN-TS] 0.0140065 0.008106 1.73 0.0968 Surf_cod[M2-TS] -0.001031 0.006698-0.15 0.8789 Device[NO -VT] 0.0007426 0.018903 0.04 0.9690 Speed 0.0003183 0.000388 0.82 0.4195 Effect Test Source Nparm DF Sum of Squares F Ratio Prob>F Tyre 17 17 0.40144656 33.0432 <.0001 Surf_code 2 2 0.00259555 1.8159 0.1843 Device 1 1 0.00000110 0.0015 0.9690 Speed 1 1 0.00048220 0.6747 0.4195 0.50 Tyre Surf_code 0.45 0.45 0.40 0.35 0.30 0.25 0.20 0.15 0.40 0.35 0.30 0.25 0.20 0.15 0.10.10.15.20.25.30.35.40.45.50 Tyre Leverage Prob>F : <0.0001 0.10.275.280.285.290.295.300.305.310.315 Surf_code Leverage Prob>F : 0.1843 Florence February 23 rd 2001 Paper 01.10 pag. 25

0.45 Speed 0.40 0.35 0.30 0.25 0.20 0.15 0.10 10 20 30 40 50 60 70 Speed Leverage Prob>F : 0.4195 The effect of speed is nearly null. The very high value of R 2 is mostly due to the two very distinct groups of data winter and summer tyres. It is then convenient to split them in two groups: summer and winter tyres. Summer TYRES : ALL SIZES Response: Mu Summary of Fit Rsquare 0.820479 RSquare Adj 0.624638 Root Mean Square Error 0.022984 Mean of Response 0.204181 Observations (or Sum Wgts) 24 Parameter Estimates Term Estimate Std Error t Ratio Prob> t Intercept 0.1880744 0.02046 9.19 <.0001 Tyre[175M-COAC] 0.0201013 0.021805 0.92 0.3764 Tyre[175S-COAC] 0.0650221 0.021805 2.98 0.0125 Tyre[195S-COAC] -0.023279 0.012053-1.93 0.0796 Tyre[225S-COAC] -0.08719 0.021805-4.00 0.0021 Tyre[CO97-COAC] 0.0016014 0.01573 0.10 0.9207 Tyre[CO98-COAC] 0.013741 0.01573 0.87 0.4010 Tyre[CO99-COAC] -0.040863 0.01573-2.60 0.0248 Tyre[COAA-COAC] 0.0126836 0.01573 0.81 0.4371 Tyre[COAB-COAC] 0.0136691 0.012956 1.06 0.3140 Surf_cod[LN-TS] -0.00246 0.011019-0.22 0.8274 Surf_cod[M2-TS] 0.018422 0.007678 2.40 0.0353 Speed 0.0005391 0.00053 1.02 0.3309 Effect Test Source Nparm DF Sum of Squares F Ratio Prob>F Tyre 9 9 0.02138372 4.4975 0.0112 Surf_code 2 2 0.00448348 4.2435 0.0431 Speed 1 1 0.00054675 1.0350 0.3309 Florence February 23 rd 2001 Paper 01.10 pag. 26

Whole-Model Test Tyre 0.25 0.25 0.20 0.20 0.15 0.15 0.10.10.15.20.25 Mu Predicted 0.10.10.15.20.25 Tyre Leverage Surf_code Speed 0.25 0.20 0.15 0.10 10 20 30 40 50 60 70 Speed Leverage Remarks: - Differences among tyres are quite strong; - The three surfaces appear as discernible; - The speed influence is null. WINTER TYRES - peak value - Response: Mu Summary of Fit RSquare 0.892803 RSquare Adj 0.774887 Root Mean Square Error 0.013868 Mean of Response 0.396931 Observations (or Sum Wgts) 22 Florence February 23 rd 2001 Paper 01.10 pag. 27

Parameter Estimates Term Estimate Std Error t Ratio Prob> t Intercept 0.3712582 0.014031 26.46 <.0001 Tyre[175W-195WS] -0.010908 0.013277-0.82 0.4305 Tyre[185W-195WS] 0.0817715 0.01495 5.47 0.0003 Tyre[195W-195WS] -0.007969 0.007353-1.08 0.3039 Tyre[225W-195WS] -0.017491 0.013277-1.32 0.2171 Tyre[175WL-195WS] -0.030553 0.013277-2.30 0.0442 Tyre[175WS-195WS] -0.020199 0.013277-1.52 0.1591 Tyre[195WL-195WS] 0.0011399 0.008252 0.14 0.8929 Device[NO-VT] 0.0007426 0.009806 0.08 0.9411 Surf_cod[LN-TS] 0.0224907 0.005452 4.13 0.0021 Surf_cod[M2-TS] -0.033495 0.005867-5.71 0.0002 Speed 0.0004603 0.000262 1.76 0.1095 Effect Test Source Nparm DF Sum of Squares F Ratio Prob>F Tyre 7 7 0.00672527 4.9957 0.0115 Device 1 1 0.00000110 0.0057 0.9411 Surf_code 2 2 0.00742139 19.2950 0.0004 Speed 1 1 0.00059332 3.0851 0.1095 Whole-Model Test 0.44 0.42 0.40 0.38 0.36 0.34 0.32 175WL 175WS 195WL 195WS 0.30.30.32.34.36.38.40.42.44 Mu Predicted Correlations with compound parameters haven t been made (only Pirelli 185W and 225W data available, for three tests). Florence February 23 rd 2001 Paper 01.10 pag. 28

Tyre 0.44 Device 0.45 0.42 0.40 0.40 0.38 0.36 0.35 0.34 0.32 0.30.34.36.38.40.42.44.46.48 Tyre Leverage 0.30.388.390.392.394.396.398 Device Leverage Prob>F: 0.0115 Surf_code 0.44 Prob>F: 0.9411 Speed 0.42 0.40 0.38 0.36 0.34 0.32 0.30 10 20 30 40 50 60 70 Speed Leverage unlikprob>f: 0.0004 Prob>F: 0.1095 Remarks: - The dependence on tyre type is quite clear; but, removing the blue cross in the right upper corner of first plot (185W, snow Mella2, by Nokian, 30 Km/h), general R 2 decreases to 0.883, and tyre leverage probability F increases to 0.4197: Florence February 23 rd 2001 Paper 01.10 pag. 29

0.44 Tyre 0.42 0.40 0.38 0.36 0.34 0.32 0.30.375.380.385.390.395.400.405.410.415 Tyre Leverage - Devices are undiscernible; - Surfaces influence is good; this is mainly due to the difference of behaviour between Mella2 snow and the surfaces Taso and Lento, as shown in the plot. Unlikely, the ranking is different from the one obtained for the only -summer-group (see relative section). - The speed influence can be considered as null. - slide value - Response: Slide Summary of Fit RSquare 0.971773 RSquare Adj 0.940724 Root Mean Square Error 46.50036 Mean of Response 1479.808 Observations (or Sum Wgts) 22 Parameter Estimates Term Estimate Std Error t Ratio Prob> t Intercept 1409.1127 47.04728 29.95 <.0001 Tyre[175W-195WS] -117.0025 44.51845-2.63 0.0252 Tyre[185W-195WS] 184.10936 50.12794 3.67 0.0043 Tyre[195W-195WS] 12.284103 24.65646 0.50 0.6291 Tyre[225W-195WS] -58.58948 44.51845-1.32 0.2175 Tyre[175WL-195WS] -131.9585 44.51845-2.96 0.0142 Tyre[175WS-195WS] -51.59448 44.51845-1.16 0.2734 Tyre[195WL-195WS] 57.123686 27.66962 2.06 0.0659 Device[NO-VT] -80.204 32.88072-2.44 0.0349 Surf_cod[LN-TS] 158.83678 18.28011 8.69 <.0001 Surf_cod[M2-TS] -295.8293 19.67423-15.04 <.0001 Speed 1.8191429 0.878774 2.07 0.0653 Effect Test Source Nparm DF Sum of Squares F Ratio Prob>F Tyre 7 7 80898.20 5.3448 0.0091 Device 1 1 12865.36 5.9499 0.0349 Surf_code 2 2 526260.97 121.6910 <.0001 Speed 1 1 9265.99 4.2853 0.0653 Florence February 23 rd 2001 Paper 01.10 pag. 30

1700 Whole-Model Test 1600 1500 1400 1300 1200 175WL 175WS 195WL 195WS 1100 1000 1000 1200 1400 1600 1800 Slide Predicted Tyre 1700 Device 1700 1600 1600 1500 1500 1400 1400 1300 1300 1200 1200 1100 1100 1000 1300 1400 1500 1600 1700 Tyre Leverage 1000 1400 1450 1500 1550 Device Leverage Prob>F: 0.0091 Surf_code Prob>F: 0.0349 Speed 1700 1600 1500 1400 1300 1200 1100 1000 10 20 30 40 50 60 70 Speed Leverage Prob>F: <0.0001 Prob>F: 0.4197 Florence February 23 rd 2001 Paper 01.10 pag. 31

Remarks: - Clear dependence on tyre; - Device effect is not meaningful; - Very strong surface effect: the same than for µ; - Very weak effect of speed. CONCLUSIONS ABOUT SNOW General conclusions about braking tests on snow are the following: - The dependence on the surface and, obviously, on the tyre type is always very relevant; - The device effect is never clear: this means that there is no evidence of systematic errors on the measurements; TRAILER TESTS ON ICE ALL SIZES Tyre Sizes: Devices: Surfaces: Speeds: 175WL 175WS 195WL 195WS Total 48 Tread Depth: always 8 mm - peak value Response: Mu Summary of Fit Rsquare 0.906503 RSquare Adj 0.816901 Root Mean Square Error 0.017516 Mean of Response 0.151771 Observations (or Sum Wgts) 48 Florence February 23 rd 2001 Paper 01.10 pag. 32

Parameter Estimates Term Estimate Std Error t Ratio Prob> t Intercept 0.1472885 0.010638 13.85 <.0001 Surf_cod[M1-T3] -0.051488 0.008603-5.99 <.0001 Surf_cod[SR-T3] 0.0217764 0.016454 1.32 0.1982 Surf_cod[T1 -T3] -0.00253 0.008862-0.29 0.7777 Device[NO -VT] -0.01305 0.011142-1.17 0.2530 Speed -0.000167 0.000274-0.61 0.5480 Tyre[175M-195WS] -0.01574 0.017958-0.88 0.3895 Tyre[175S-195WS] 0.0048306 0.01233 0.39 0.6987 Tyre[175W-195WS] 0.0651217 0.017958 3.63 0.0013 Tyre[185S-195WS] -0.037804 0.017649-2.14 0.0425 Tyre[185W-195WS] 0.0145433 0.013157 1.11 0.2799 Tyre[195S-195WS] -0.034302 0.009788-3.50 0.0018 Tyre[195W-195WS] 0.0219425 0.007731 2.84 0.0091 Tyre[225S-195WS] -0.060994 0.012614-4.84 <.0001 Tyre[225W-195WS] -0.015878 0.012614-1.26 0.2202 Tyre[CO97-195WS] -0.038355 0.012614-3.04 0.0056 Tyre[CO98-195WS] -0.023567 0.012614-1.87 0.0740 Tyre[CO99-195WS] -0.060356 0.012614-4.79 <.0001 Tyre[COAA-195WS] -0.032189 0.012614-2.55 0.0175 Tyre[COAB-195WS] -0.005706 0.008246-0.69 0.4956 Tyre[COAC-195WS] -0.010481 0.012614-0.83 0.4142 Tyre[175WL-195WS] 0.052709 0.017958 2.94 0.0072 Tyre[175WS-195WS] 0.073945 0.017958 4.12 0.0004 Tyre[195WL-195WS] 0.0482543 0.009385 5.14 <.0001 Effect Test Source Nparm DF Sum of Squares F Ratio Prob>F Surf_code 3 3 0.01660527 18.0407 <.0001 Device 1 1 0.00042090 1.3718 0.2530 Speed 1 1 0.00011391 0.3713 0.5480 Tyre 18 18 0.05159785 9.3430 <.0001 0.25 Whole-Model Test 0.20 0.15 0.10 0.05.05.10.15.20.25 Mu Predicted R 2 = 0.907 Florence February 23 rd 2001 Paper 01.10 pag. 33

0.25 Surf_code 0.25 Device 0.20 0.20 0.15 0.15 0.10 0.10 0.05 0.05.08.10.12.14.16.18.20.22 Surf_code Leverage Prob>F: < 0.0001.125.135.145.155.165 Device Leverage Prob>F: 0.2530 0.25 Speed 0.25 Tyre 0.20 0.20 0.15 0.15 0.10 0.10 0.05 0.05 10 20 30 40 50 60 70 Speed Leverage Prob > F : 0.548.075.100.125.150.175.200.225 Tyre Leverage Prob>F: < 0.0001 Remarks: - Good surface effect; - Very good Tyre effect. Winter tyres performance is higher, but the difference with summer is not so strong as in the case of snow; - Speed and device influences are null. Florence February 23 rd 2001 Paper 01.10 pag. 34

- slide value - Response: Slide Summary of Fit RSquare 0.963459 RSquare Adj 0.92844 Root Mean Square Error 38.66876 Mean of Response 406.688 Observations (or Sum Wgts) 48 Parameter Estimates Term Estimate Std Error t Ratio Prob> t Intercept 404.4799 23.48524 17.22 <.0001 Surf_cod[M1-T3] -159.7971 18.99165-8.41 <.0001 Surf_cod[SR-T3] 77.718633 36.3244 2.14 0.0428 Surf_cod[T1-T3] 26.533715 19.56302 1.36 0.1876 Device[NO -VT] 5.1521267 24.59762 0.21 0.8359 Speed -1.559657 0.604553-2.58 0.0164 Tyre[175M -195WS] -169.207 39.64357-4.27 0.0003 Tyre[175S-195WS] -47.72035 27.22068-1.75 0.0924 Tyre[175W-195WS] 145.58298 39.64357 3.67 0.0012 Tyre[185S-195WS] -67.63669 38.96186-1.74 0.0954 Tyre[185W-195WS] 121.10106 29.04467 4.17 0.0003 Tyre[195S-195WS] -85.47323 21.60826-3.96 0.0006 Tyre[195W-195WS] 91.144389 17.06704 5.34 <.0001 Tyre[225S-195WS] -175.8039 27.84588-6.31 <.0001 Tyre[225W-195WS] 19.761621 27.84588 0.71 0.4847 Tyre[CO97-195WS] -141.2339 27.84588-5.07 <.0001 Tyre[CO98-195WS] -95.99688 27.84588-3.45 0.0021 Tyre[CO99-195WS] -179.6834 27.84588-6.45 <.0001 Tyre[COAA-195WS] -126.0339 27.84588-4.53 0.0001 Tyre[COAB-195WS] -39.17249 18.20467-2.15 0.0417 Tyre[COAC-195WS] -75.27988 27.84588-2.70 0.0124 Tyre[175WL-195WS] 115.63598 39.64357 2.92 0.0076 Tyre[175WS-195WS] 254.09898 39.64357 6.41 <.0001 Tyre[195WL-195WS] 145.45855 20.71777 7.02 <.0001 Effect Test Source Nparm DF Sum of Squares F Ratio Prob>F Surf_code 3 3 121162.79 27.0102 <.0001 Device 1 1 65.60 0.0439 0.8359 Speed 1 1 9951.99 6.6556 0.0164 Tyre 18 18 838581.46 31.1568 <.0001 800 Whole-Model Test 700 600 500 400 300 200 100 100 200 300 400 500 600 700 800 Slide Predicted R 2 = 0.963 Florence February 23 rd 2001 Paper 01.10 pag. 35

800 Surf_code 800 Device 700 700 600 500 M1 600 500 400 400 300 300 200 200 100 150 200 250 300 350 400 450 500 550 600 Surf_code Leverage Prob > F: < 0.0001 100 340 350 360 370 380 390 400 410 420 Device Leverage Prob > F: 0.836 800 Speed 800 Tyre 700 700 600 600 500 500 400 400 300 300 200 200 100 10 20 30 40 50 60 70 Speed Leverage 100 100 200 300 400 500 600 700 800 Tyre Leverage Remarks: Prob > F: 0.0164 Prob > F: 0.0001 - Good surface influence (particularly, M1 is separated and weak ); - Device influence is not feasible; - Speed seems to decrease the slide force value; - There is a very strong and sensible influence of tyre type. Florence February 23 rd 2001 Paper 01.10 pag. 36

CONCLUSIONS ABOUT ICE - The specific device does not influences the test in a dramatic way; - A strong dependence on surface occur; BRAKING TESTS ON VEHICLE WITH/WITHOUT ABS Always within VERT project, braking tests with a vehicle have been performed by CETE on wet and dry asphalt, with and without the use of ABS system, braking at a speed of 80Km/h. The tyres tested are the same six 195/65 R15 Summer tyres used in trailer tests to investigate the tread compound effect (plus a Winter tyre). Several plots were traced, for each possible combination of test on vehicle related test on trailer, wet surface. In most cases, data distribution was not significant at all. The few interesting cases are showed below, where distances are reported in percentage, with reference to AA compound ABS on, separately for wet and dry: Distance% By Peak value Distance% By Peak value 106 105 104 103 102 101 100 99 98 97 96 95 3400 3500 3600 3700 3800 Picco Trailer: Pirelli, 1 mm water depth, 50 Km/h; Vehicle: ABS on Distance% By Peak value 106 105 104 103 102 101 100 99 98 97 96 95 3500 3600 3700 3800 3900 Picco Trailer: VTI, 1mm water depth, 100 Km/h; Vehicle: ABS on Distance% By Slide 106 105 104 140 103 102 101 135 100 99 98 130 97 96 95 2850 2950 3050 3150 3250 Picco Trailer: VTI, 7 mm water, 100Km/h; Vehicle: ABS on 125 1650 1750 1850 1950 2050 Slide Trailer: Pirelli, 1 mm water, 50 Km/h; Vehicle: ABS off Florence February 23 rd 2001 Paper 01.10 pag. 37

Distance% By Slide 140 135 130 125 1350 1400 1450 1500 1550 Slide Trailer: Pirelli, 1mm, 90 Km/h; Vehicle: ABS off Some of the conclusions of CETE find a comfirmation in the trailer braking tests database. Particularly: - In CETE report one of the comments is: The greater efficiency of ABS on a wet road surface compared to a dry surface (30% increase in stopping distance without ABS on a wet surface compared to 11% on a dry road surface). This point might be retrieved on the curves longitudinal force slippage generated with trailer tests. In effect, let see the statistical distribution, on wet and on dry tests, of the peak/slide ratio: - Wet surface Florence February 23 rd 2001 Paper 01.10 pag. 38

- Dry surface The peak of distribution in tests on wet is shifted towards higher values than the dry ones. This comfirms the greater increase of performance of ABS on wet. - ABS performance increase with respect to normal braking has been valued by CETE as higher in a intermediate range of velocities. This is not in discord with the considerations about the increase of peak/slide ratio with, done previously. Florence February 23 rd 2001 Paper 01.10 pag. 39

CONCLUSION The expected testing conditions effects, particularly on wet asphalts (the biggest sub-group of available data), have been fully retrieved. In some cases, the ranking among tyres with same structure and different tread compound, provided with trailer tests, was coherent with that obtained with a group of braking tests on a vehicle. The influence of testing conditions, such as velocity, speed, water and tread depths, ground surface, is definitely correct in the case of wet asphalt tests, and gives much information about the variation of available friction. The influence of the different devices used can be recognized, but it is not judged high enough to spoil the validity of tests; in any case, it is automatically taken in account in the statistical evaluations. Different surfaces also dramatically influences the performance, especially in the case of snows and ices, where the effect of the other variables (tread depth, velocity) cannot be investigated (it was out of the purposes of the test activity). The quantitative characterisazion of surface would need a more exhaustive activity. Dry VERT activity has been mostly focused on critical conditions regarding available friction, such as wet, snowed and iced surfaces; thus, only a few number of tests has been performed on dry asphalt, with different tread compounds but also very different structures (from 175/65R14 up to 225/45R17). However, the most relevant effect seems to be a difference between the sensitivity of µ MAX and slide values in relation to the tyre properties, generating a µ MAX /Slide ratio generally proportional to µ MAX : Wet A big amount of tests has been carried out on wet surfaces. Those of them which have passed the interpolation and check phases can be are 724 tests with different tyre sizes and structures, tread depths, water depths, speeds, surfaces. This group of data is statistically very relevant and leads to an excellent coerence of results if referred to the testing conditions. Particularly: - The main effect of reduction of peak force value with the increase of water depth and speed and with the reduction of the tire tread depth, is fully noticeable and is in the correct directions. The effect of the tread thickness appears to be stronger than the water depth one. - Always for µmax value, Tyre different properties effect also is evident; the worse behaviour of winter sizes on wet is acceptable, while more surprising is the collocation of 225/45 R17 Summer tyres among more normal sizes. The same ranking one can notice in the leverage plot can be retrieved in the table of parameter estimates. - The most relevant effects on braking behaviour slope at small slippages are the changes in surface and tyre type. Speed and water depth effects are much smaller, but in the correct directions. Florence February 23 rd 2001 Paper 01.10 pag. 40

- With increasing speed, the peak/slide ratio tends to increase, emphasising the gain due to the use of an hypothetical ABS system. - This consideration is comfirmed by the vehicle tests performed with and without use of ABS system, on dry and wet asphalt. Below, the found general effects of wet testing conditions on performance parameters are summarised: Snow For what concerns trailer tests on ice for different tyre sizes, the main found evidences are: - Generally, the effect of the speed on the achieved performances seems to be nearly null. - Very (obviously) different behaviour between summer and winter tyres retrieved. - Strong influence of the tyre type also withing the group summer and the group winter - Definitely strong influence of the surface (type of snow) on behaviour The tyres with same structure and different compounds have highlighted a strong influence of the surface behaviour. Ice - The specific device does not seem to influence the test in a dramatic way; - A strong dependence on surface type has been highlighted. Vehicle The braking tests on XANTIA, executed by CETE, on wet and dry surfaces, activating and deactivating the ABS system, were generally in good agreement with the compounds ranking defined with the trailer tests. On the other side, the attempt of establishing a direct relation between trailer tests results (µmax and slide values) was much less productive. Florence February 23 rd 2001 Paper 01.10 pag. 41

ACKNOWLEDGMENTS Acknowledgments are due to all the VERT partners, without whose co-operation it would have been impossible to collect a so relevant and useful amount of information. CONTACT - Diego Donadio Pirelli Tyres Tel +39 02 6442 7929 e-mail diego.donadio@pirelli.com - Diego Speziari Pirelli Tyres Tel +39 02 6442 9871 e-mail diego.speziari@pirelli.com REFERENCES - JMP3.1 Statistics and Graphics Guide, SAS Institute Inc. - Techniques for determining the parameters of a two-dimensional tire model for the study of the ride comfort, G. Matrascia, Tire science and technology, 1997, Vol. 25, Number 3 Florence February 23 rd 2001 Paper 01.10 pag. 42