Extended Summary 本文は pp.761–767

Detection of Road Surface Conditions Using Tire Noise from Vehicles

Wuttiwat Kongrattanaprasert Student Member (The University of Electro-Communications, wuttiwat@ew3.ee.uec.ac.jp)

Hideyuki Nomura Non-member (The University of Electro-Communications, nomu@ee.uec.ac.jp)

Tomoo Kamakura Non-member (The University of Electro-Communications, kamakura@ee.uec.ac.jp)

Koji Ueda Non-member (Nagoya Electric Works Co., LTD, ueda.koji@nagoya-denki.co.jp)

Keywords: road surface conditions, tire noise, frequency analysis, intelligent transportation system

The detection of road surface conditions is an important processfor efficient road management. In particular, in snowy seasons, priorinformation about the road conditions, such as an icy state, helpsroad users or automobile drivers to obviate serious traffic accidents.This paper is basically in line with the approach of Ueda et al. Toknow general tendencies of the power spectrum, we recorded a num-ber of tire noises at near The University of Electro-Communications(UEC) and near Sapporo city. We detected the state of the road sur-face: i.e., dry, wet, or snowy state. Tire noise emitted from movingvehicles on the road surface by various mechanisms was recordedwith a PCM recoder. The noise signals are processed through ahigh-pass filter with a cut off frequency of 300 Hz to remove un-necessary signals such as engine noises and wind noise, and areconverted into the power spectrum by fast Fourier transform (FFT).After that, we determine the frequency at which the power spec-trum reaches the maximum. Additionally, our predicting approachrelies on the normalized magnitude of the spectrum at a frequencyof 1.5 kHz and on a frequency at which the normalized magnitudetakes a value of 0.5. The effectiveness of these three classificationindicators are verified by noise data samples obtained at the threeexperimental locations and are compared with visual inspections ofactual road surfaces.

For instance, the cumulative distribution curves P̄( f ) obtainedfrom passing vehicles for five-minute data near UEC are shown inFig. 1. The magnitudes for the wet state are all lower than those forthe dry state through all frequencies. We propose two classificationindicators in accordance with the fact that easy classification of the

Fig. 1. Cummulative curves of the power spectrum P̄( f )for passing vehicles in 5 min (UEC)

states is feasible when the difference between the two distributioncurves is the largest. One indicator is the normalized mangitude ofoverline P̄( f ) at a frequency of 1.5 kHz or the ‘indicator at 1.5 kHz’.The other indicator is the frequency at which the normalized magni-tude takes a value of 0.5 or the ‘indicator at 0.5’. Without changingthe observation location, we detected tire noise for the road surfacein different states. These surface states were monitored visually us-ing a video camera. It should be noted that both the indicators in-deed represent the changes of the surface states.

Figure 2 shows the natural transition process between the threedifferent states. It is also allowed to change from dry to wet and wetto snowy. However, the direct transition from the snowy state to thedry state is not allowed: i.e., the wet state always exists in the pro-cess. For the wet state, the indicator at 0.5 has the highest frequencyof the three, while the wet state in the natural process exists betweenthe other two states. This interchanged situation makes it difficult toclassify successfully the three states. This is important informationfor classifying the dry and slushy states.

Using the observed data, accurate classification into three statesseems to be feasible by employing either indicator on the basis ofcertain threshold values. The accuracy of correct detection is lowoverall, the use of the indicator at 0.5 gives the highest accuracy,and the peak-frequency method has the lowest accuracy. The mainreason why relatively high detection errors arise is that the road wascovered with slushy water and was predicted to be dry in the transi-tion process.

Four kinds of road surfaces are exhibited. It is noted that thecurves for the slushy road are more obviously scattered than thecurves of the remaining three states. Therefore, the accuracy in clas-sification is improved by introducing the standard deviation σ.

Fig. 2. Transition diagram for the different surfacestates. Fl and Fh are the threshold frequencies for theindicator at 0.5

– 13 –

Paper

Detection of Road Surface Conditions Using Tire Noise from Vehicles

Wuttiwat Kongrattanaprasert∗ Student Member

Hideyuki Nomura∗ Non-member

Tomoo Kamakura∗ Non-member

Koji Ueda∗∗ Non-member

Information on road surface conditions is important and helpful for road users such as automobile drivers, partic-ularly in snowy seasons. In practice, the surface conditions depend greatly on the weather, road users, location, andother relevant factors. This report is concerned with the reliable detection of the surface conditions using tire noisefrom road vehicles. The road/tire noise emitted from moving vehicles varies momentarily depending on the road sur-face properties. Then, it may be possible to passively and easily detect the state of the road surface: i.e., dry, wet, orsnowy state. To detect tire noise, we used a commercially available microphone as an acoustic sensor, which enabledus to easily reduce the cost and size in realizing a practical system for detecting road surface conditions. We proposea couple of simple detection methods to classify the conditions into several categories of state and to improve the clas-sification accuracy. From the experimental results obtained in snowy areas, it has been demonstrated that an accuracyof up to about 81% is attained in predicting the road surface states using only tire noise data.

Keywords: road surface conditions, tire noise, frequency analysis, intelligent transportation system

1. Introduction

The detection of road surface conditions is an importantprocess for efficient road management. In particular, insnowy seasons, prior information about the road conditions,such as an icy state, helps road users or automobile driversto obviate serious traffic accidents. To predict road surfaceconditions 3 hours and 24 hours ahead, Saegusa and Fuji-wara recently proposed a promising method using weatherforecast data and field data (1). They achieved a great im-provement in the accuracy of predicting the surface states,particularly dry and frozen states, compared with almost thesame methods previously reported. Different views and treat-ments were adopted in the past by McFall and Niittula (2), whoused images of road surfaces and traffic noise from vehiclesto classify road surface conditions without weather data (3).Unfortunately, their approach suffered from systematic prob-lems of high cost and unstable accuracy. For cost reduction,the detection of the surface conditions using tire noise fromvehicles has so far been performed. Kubo et al. presented afrequency spectrum method that enables the determination ofthe frequency components and sound pressure levels of tirenoise (4). However, the pressure levels depend greatly on, forexample, the size of the vehicle and its engine sound, thus it isnot always stable in the detection of the conditions using onlythe magnitude of the spectrum. To reduce such instability,Ueda et al. introduced the normalized power spectrum basedon the ratio of the frequency spectrum to the total power (5).Experimentally, they found that the high-cut frequency forclassifying dry, wet, and snowy conditions is 2 kHz in the

∗ Department of Electronics, The University of Electro-Communications1-5-1, Chofugaoka, Chofu 182-8585

∗∗ 19-1, Mentoku, Miwa-cho, Ama-gun, Aichi 490-1294

normalized power spectrum. The thus determined frequencyseems to change depending on the measurement location.

This paper is basically in line with the approach of Uedaet al. To know general tendencies of the power spectrum, werecorded a number of tire noises at different locations. Thenoise signals are processed through a high-pass filter with acut off frequency of 300 Hz to remove the engine noises ofpassing road vehicles and are converted into the power spec-trum by fast Fourier transform (FFT). After that, we deter-mined the frequency at which the power spectrum reachesthe maximum. Additionally, our predicting approach relieson the normalized magnitude of the spectrum at a frequencyof 1.5 kHz and at a frequency at which the normalized mag-nitude takes a value of 0.5. The effectiveness of these threeclassification indicators are verified by noise data samplesobtained at the three experimental locations and are com-pared with visual inspections of actual road surfaces.

2. Experimental Conditions

Tire noise from moving vehicles was recorded at three dif-ferent locations: one is a sidewalk of a two-lane city road nearthe campus of The University of Electro-Communications.We call this location ‘UEC’ for short. Two other observa-tion sites were on the sides of four-lane national roads nearSapporo city. The roads are all asphalted.

The hardware system of the measurement we performednear UEC is shown in Fig. 1. Tire noise emitted from movingvehicles on the road surface by various mechanisms such asair pumping was recorded with a PCM recorder, which wasset on a tripod on the sidewalk. A built-in microphone wasset at a 2 m height from the road surface level so that it wasdirected towards passenger vehicles at 45◦ with respect to theroad surface. Vehicles passed by at 40 km/h on average. The

c© 2009 The Institute of Electrical Engineers of Japan. 761

Fig. 1. Experimental setup for detecting tire noise fromvehicles (UEC)

recorder digitally sampled sound signals at a frequency of22.05 kHz with 16 bit quantization. As a rule of thumb, weclassify the surface conditions into three categories (1) (6)

(i) Dry: A road surface with no water, that is truly dry.(ii) Wet: A road that is covered with water and remains

wet. Vehicles splash water as they pass by and thetire tracks remain for a while. This condition includesslushy water from melted snow.

(iii) Snow-compacted: A snowy surface that has becomecompacted owing to passing vehicles. The road sur-face looks completely white, including the wheeltracks.

At the UEC observation site, we obtained noise data for ve-hicles with summer or regular tires only when the road wasdry or wet due to rain. The data including tire noise gener-ated from snowy surfaces were obtained at the two observa-tion sites near Sapporo city. All passenger vehicles seemedto have winter or studless tires. The roads have four lanes,and vehicles were traveling at 60 km/h to 80 km/h on aver-age. Basically, the data acquisition system was the same asthat described above, except for the microphone being set ata height of 4.4 m from the ground. Tire noise was recordedcontinuously for more than one hour.

3. Classification Methods

3.1 Peak Frequencies Sound signals recorded witha microphone are fed to a high-pass filter with a cut off fre-quency of 300 Hz to remove unnecessary signals such as en-gine noise and wind noise. Then, FFT is applied to obtain apower spectrum p( f ) ( f is frequency) for the tire noise signalfrom each vehicle.

Incidentally, we usually observe that the timbre of tirenoise is dependent on road conditions. When a road haswater on its surface, for example, the pressure level of tirenoise generally increases because of water splashing. Addi-tionally, high-frequency components seem to be auditorilyincreased on the whole compared with the case when thesurface is dry. We then first focus our attention on the fre-quency at which each tire noise attains a peak in its powerspectrum. All spectra are obtained by executing FFT on thesignal waveform that lasts for 1.5 s. We extracted manuallythe individual waveforms from time history records observedover about one hour using a free sound engine program. Fig-ure 2 shows the peak frequencies of about 1500 vehicles thatpassed by the UEC observation point. Two different condi-tions, the ‘wet’ and ‘dry’ states on the road, are the targetsof classification. Obviously, the frequency varies from vehi-cle to vehicle, and it appears difficult to obtain informationon the road surface conditions from these randomly scatteredfrequencies. However, upon averaging the frequencies overevery 20 vehicles, a definite difference appears. As can be

(a)

(b)

Fig. 2. Peak frequencies for all vehicles in the powerspectra of their tire noise

Fig. 3. Peak frequencies averaged over every 20 vehi-cles (UEC)

seen in Fig. 3, the peak frequencies are within the range of0.8 kHz to 1 kHz for the ‘wet’ state and are about 0.3 kHzhigher than the frequencies for the ‘dry’ state, which are con-centrated around 0.6 kHz. This observation supports our au-ditory sense described above.

Tire noises from trucks and buses are generally louder thanthose from the remaining small cars, and difference in sig-nal characteristic might exist between the two vehicle groups.Actually, the tire signals we observed included those from 20small cars, 10 trucks, 2 buses, and 2 motorcycles in the first5 minutes. From all the data, we picked out only the signalsfrom small cars using the sound engine program. Figure 4shows the time history records of the peak frequencies aver-aged over every 20 small cars. Apparently, almost the samepatterns are obtained for the two different road states: i.e., thepeak frequencies for the wet state are 0.2 kHz or much higherthan those for the dry state. Consequently, the following twoimportant findings are obtained. First, it is of great neces-sity to execute averaging for the data obtained from vehicles

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Detection of Road Surface Conditions Using Tire Noise

Fig. 4. Peak frequencies averaged over every 20 smallcars (UEC)

in order to extract distinct difference among various states ofroad surfaces. In fact, such averaging should be done overtime rather than the number of vehicles because road surfaceconditions are time-varying as they depend on the weather.Empirically, 5-minute averaging is recommended. Second,there is no great difference between the data of the peak fre-quencies measured from all the vehicles and from only smallcars. There seems to be no need for the pre-processing thatextracts data of only small cars.

3.2 Proposed Methods Since the sound pressurelevel depends greatly on, for example, the size of the vehi-cle and its engine sound, it is not sufficiently stable for de-tecting the conditions on the basis of only the magnitude ofthe spectrum. Prior to taking the next step, we introduce thefollowing function that can be defined as (5)

P( f ) =

∫ f

flp( f ′)d f ′

∫ fhfl

p( f ′)d f ′· · · · · · · · · · · · · · · · · · · · · · · · · · · · (1)

where fl = 300 Hz is the low-cut frequency. Generally, tirenoise does not significantly contain frequency componentsgreater than 10 kHz; then, the upper limit of integration withrespect to frequency is determined to be fh = 10 kHz. Here-after, we refer to P( f ) as the cumulative distribution of thepower spectrum. For instance, the cumulative distributioncurves obtained from passing vehicles for five-minute datanear UEC are shown in Fig. 5.

Both curves first increase in magnitude relatively slowlywith frequency, then the rates of increase become abrupt near1 kHz. After that, they slow down again near 4 or 5 kHz.Such monotonic increment tendencies of P( f ) resemble aGaussian distribution function. Additionally, the magnitudesfor the wet state are all lower than those for the dry statethrough all frequencies. This means that the wet state pre-dominates over the dry state in higher frequency components.

From the cumulative distributions in Fig. 5, we proposetwo classification indicators in accordance with the fact thateasy classification of the states is feasible when the differ-ence between the two distribution curves is the largest. Oneindicator is the normalized magnitude of P( f ) at a frequencyof 1.5 kHz. The other indicator is the frequency at which thenormalized magnitude takes a value of 0.5. Hereafter, werefer to these as the ‘indicator at 1.5 kHz’ and the ‘indica-tor at 0.5’, respectively. In Fig. 5, the indicator at 1.5 kHz isdetermined to be 0.5 and 0.27 for the dry and wet states, re-spectively. The indicator at 0.5 is determined to be 1.5 kHzand 2.2 kHz for the respective states.

Fig. 5. Cumulative curves of the power spectrum forpassing vehicles in 5 min (UEC)

4. Experimental Results

To evaluate the effectiveness of the proposed classificationmethods, we executed signal processing using tire noise de-tected near UEC and Sapporo city. All the signals last con-tinuously for 50 minutes. Additional data for 3 days wereobtained at the sides of a different road near Sapporo city.

4.1 Observation for 50 Minutes Without changingthe observation location, we detected tire noise signals for atleast 50 minutes for the road surface in different states. Fig-ure 6 shows the time histories of the ‘indicator at 1.5 kHz’ forthree different states. These data are averaged magnitudesover every 5 minutes. It is demonstrated that the proposedindicator explicitly exhibits differences in magnitudes for thethree surface states, although the magnitude of the indicatoritself changes from location to location.

For example, the magnitude remains within the range of0.5 to 0.6 for the dry state and within the range of 0.3 to 0.4for the wet state near UEC. In contrast, the values are smallernear Sapporo city, being around 0.4 and within the range of0.2 to 0.3, respectively. Even so, the tendencies of changein magnitude depending on the road surface conditions maybe independent of location as well as the kind of tire. Theindicator takes the largest value for the snowy state and thesmallest value for the wet state.

Data processing has been executed by the second classifi-cation method, using the ‘indicator at 0.5’, for the same tirenoise data. The results are shown in Fig. 7. The order ofmagnitude is interchanged compared with Fig. 6: i.e., the wetsurface takes the highest frequency and the snowy surface thelowest. The frequencies for the dry state remain within the in-termediate range of the three states. Using the observed data,accurate classification into three states seems to be feasibleby employing either indicator because the states are perfectlyseparable on the basis of certain threshold values.

4.2 Observation for 24 Hours It is of interest to ex-tend the present methods to the daily observation of road sur-face states that may change with time and weather.

To determine whether the indicators can actually indicatethe changeable surface states, we examined typical one-daysound data of a long-time observation, which include all threestates of road surfaces at another location near Sapporo city.Figure 8 shows the time histories of the two indicators. The

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(a)

(b)

Fig. 6. Time histories of the ‘indicator at 1.5 kHz’ for50-minute observation near UEC (a) and Sapporo city (b)

(a)

(b)

Fig. 7. Time histories of the ‘indicator at 0.5’ for 50-minute observation near UEC (a) and Sapporo city (b)

data collection started at 0 a.m. and ended at 0 a.m. of thenext day. These surface states were monitored visually usinga video camera. It should be noted that both the indicatorsindeed represent the changes of the surface states. Overall,the sample data are scattered in the early morning from 2to 4 a.m., probably because the number of vehicles passingthrough the observation site was small. However, the dataobtained using the indicator at 0.5 show relatively less scat-tering results throughout the day than the data obtained usingthe indicator at 1.5 kHz. Interestingly, even if the observation

(a)

(b)

Fig. 8. One-day observation near Sapporo city. The in-dicator at 1.5 kHz (a) and the indicator at 0.5 (b) are pre-sented. The observation started at 0 a.m. and ended at0 a.m. of the next day

with the camera is unavailable, it can be roughly expected,from the figures, that the road surface changed from thesnowy state to the wet state in the morning, remained wet un-til 2 p.m., and subsequently changed to the dry state. At anyrate, the indicator at 0.5 takes a frequency of 1.46 kHz on av-erage for the snowy state from 0 a.m. to 9:30 a.m. Likewise,for the wet and dry surfaces, it takes 2.20 kHz and 1.94 kHz,respectively.

We then propose the threshold frequencies of classifica-tion using arithmetic averaging as follows: the frequenciesare Fl = (1.46 + 1.94)/2 = 1.70 kHz between the snowy anddry states, and Fh = (1.94 + 2.20)/2 = 2.07 kHz between thedry and wet states.

5. Classification into Three States

Figure 9(a) shows the natural transition process betweenthe three different states. The snowy state may change to thewet state upon temperature elevation and to the dry state uponwater evaporation when the temperature is further elevated. Itis also allowed to change from dry to wet and wet to snowy.However, the direct transition from the snowy state to the drystate is not allowed: i.e., the wet state always exists in the pro-cess. Unfortunately, the order of the states in the indicatorswe propose is different from such a natural transition orderjust mentioned. As shown in Fig. 9(b), the wet and dry statesare interchanged, i.e., for the wet state, the indicator at 0.5 hasthe highest frequency of the three, while the wet state in thenatural process exists between the other two states. This in-terchanged situation makes it difficult to classify successfully

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Detection of Road Surface Conditions Using Tire Noise

(a) (b)

Fig. 9. Transition diagram for the different surfacestates. Fl and Fh are the threshold frequencies for theindicator at 0.5. Specifically, Fl = 1.70 kHz and Fh =2.07 kHz in the present experiment

(a)

(b)

(c)

Fig. 10. Time histories of the indicator at 0.5 and theroad surface temperature for the three-day observationnear Sapporo city. Data (b) is the same as in Fig. 8(b).Data (a) to (c) were taken over three days at the sameobservation location

(a)

(b)

Fig. 11. Flowchart for a simple method of classifyingroad surface states using the indicator frequency at 0.5(a), and typical distribution curves for four kinds of sur-face states (b)

the three states. In fact, at around 10 a.m. in Fig. 8(b), the in-dicator takes almost the same frequencies as those in the drystate after 2 p.m., but it does not mean that the road surface isdry in the former time zone: the frequency happens to tem-porarily take a value of 2 kHz in the transition process fromthe snowy state to the wet state. This is important informa-tion for classifying the dry and slushy states.

Three-day observation data for the indicator at 0.5, includ-ing sound signals from the preceding day and the followingday of the day corresponding to Fig. 8, are shown in Fig. 10.Data (b) is the same as that in Fig. 8(b). Using a simple classi-fication method based on only the threshold frequencies andthe flowchart shown in Fig. 11(a), we attempted to classifythe road surface into the three states. Table 1 shows the re-sults, where the results of the other two methods of using theindicator at 1.5 kHz and the peak frequency of the tire noisespectrum are listed for comparison. Although the accuracy ofcorrect detection is low overall, the use of the indicator at 0.5gives the highest accuracy, and the peak-frequency methodhas the lowest accuracy. The main reason why relatively highdetection errors arise is that the road was covered with slushywater at around 10 a.m. and was predicted to be dry in thetransition process from the snowy state to the wet state, asshown in Fig. 9(b).

As is described in Sec. 2, a slushy surface is expedientlycategorized into the wet state. Actual surfaces for such roads

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Table 1. Experimental results of detecting the road surface states over three days using 5-minute sound signals

Threshold values Accuracy [%]

Methods Dry Wet Snowy 1st day 2nd day 3rd day total (average)

Indicator at 1.5 kHz 0.32 ∼ 0.38 < 0.32 > 0.38 77.8 71.5 43.8 64.4

Indicator at 0.5 [kHz] 1.70 ∼ 2.07 > 2.07 < 1.7 75.4 93.7 50.0 73

Peak frequency [kHz] 0.61 ∼ 0.76 > 0.76 < 0.61 65.8 58.3 34.8 53.0

Table 2. Experimental results of detecting the road surface states over three days using 5-minute sound signals

Threshold values Accuracy

Frequency [kHz] Standard deviation: σt [Hz] [%]

Wet Snowy Dry Slushy 1st day 2nd day 3rd day Total (average)

> 2.07 < 1.70 < 151 > 151 79.5 95.8 66.7 80.7

Fig. 12. Flowchart for an advanced method of classify-ing road surface states using the indicator frequency at0.5. The information of the standard deviation is included

are not always covered with slush: i.e., parts of the surfaceare still snowy and other parts are already dry owing to waterevaporation.

Additionally, not all vehicles pass over the slushy surfaces.Figure 11(b) shows typical cumulative distributions for anobservation time of about 30 minutes. Four kinds of roadsurfaces, wet, dry, snowy, and slushy states, are exhibited. Itis noted that the curves for the slushy road are more obvi-ously scattered than the curves of the remaining three states,particularly the curves of the dry state.

Therefore, it seems to be feasible to discriminate the dryand slushy surfaces by introducing some statistical measures,such as the standard deviation σ. The flowchart in Fig. 12 isan advanced classification method that makes use of the sta-tistical factor σ. Specifically, the value of σ for the indicatorat 0.5 was determined to be 151 Hz from one-hour data ataround 10 a.m. in Fig. 10(b).

Table 2 shows the results obtained using the advancedclassification method. It is already shown in Table 1 thatwhen the indicator at 0.5 takes a value between 1.7 kHz and2.07 kHz, the road surface is either dry or slushy. In this case,we use the standard deviation σ for judging the classification.If σ < 151 Hz, the surface is dry. Otherwise, the surface is inthe slushy state. Table 1 reveals that the accuracy in classifi-cation is improved by introducing the standard deviation.

6. Conclusions

We presented new methods for classifying road surfaceconditions using only the tire sound noise emitted from mov-ing vehicles. A couple of detection indicators were proposed:the normalized magnitude at 1.5 kHz in the cumulative distri-bution and the frequency at which the normalized magnitudetakes a value of 0.5. From various field experiments, it wasfound that the two indicators have almost the same classifi-cation accuracy. It was also demonstrated that averaging ofthe noise data is important in order to extract distinct differ-ences among various states of road surfaces. At the presenttime, classification accuracy is, at most, 81%. However, theaccuray may be improved by incorporating the natural tran-sition process and meteorological information such as roadtemperature. Further studies concerning such problems areunder way.

(Manuscript received Nov. 13, 2008,revised Feb. 21, 2009)

References

( 1 ) A. Saegusa and Y. Fujiwara: “A Study on Forecasting Road Surface Condi-tions Based on Weather and Road Surface Data”, IEICE Trans. INF. & SYST.,Vol.E90-D, No.2, pp.509–516 (2007-2)

( 2 ) K. McFall and T. Niittula: “Results of AV Winter Road Condition SensorPrototype”, The 11th Standing International Road Weather Commission inSapporo (2002-1)

( 3 ) AerotechTelub and Dalarna University: “Final Report on Signal and ImageProcessing for Road Condition Classification”, pp.1–30 (2002-2)

( 4 ) T. Kubo, C. Shimomura, and T. Haruyama: “Discriminant Analysis Systemfor Winter Roads Utilizing Automobile Tire Sounds”, AIPCR·PIARC, The12th International Winter Road Congress in Italy (2006-3)

( 5 ) K. Ueda, K. Nakamura, H. Onodera, N. Konagai, and T. Kamakura: “De-velopment of Road Condition Detecting Sensor Using Vehicle RunningSounds”, The Papers of Technical Meeting on Intelligent Transport Systems,IEE Japan, No.ITS-07-13, pp.25–30 (2007) (in Japanese)

( 6 ) M. Yamada, K. Ueda, I. Horiba, and N. Sugie: “Discrimination of the RoadCondition Toward Understanding of Vehicle Driving Environments”, Intel-ligent Transportation Systems, IEEE Transaction, Vol.2, No.1, pp.26–31(2001-3)

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Detection of Road Surface Conditions Using Tire Noise

Wuttiwat Kongrattanaprasert (Student Member) received hisB. Eng degree in Electrical Engineering fromRajamangala Institute of Technology, Thailand, inMay 1995. In March 2001, he received hisM. Eng degree in Electrical Engineering fromKing Mongkut’s University of Technology, Thon-buri, Thailand. He is currently a PhD student ofElectronic Engineering at The University of Electro-Communications (UEC), Japan. His research inter-ests are in the detection of road surface conditions us-

ing tire noise from vehicles and nondestructive determination using forcevibration and ultrasound.

Hideyuki Nomura (Non-member) received his B. E., M. E., andDr. Eng. degrees in Electronic Engineering fromthe University of Electro-Communications in 1996,1998, and 2001, respectively. He was a ResearchAssociate at the Department of Information and Sys-tems Engineering, Kanazawa University from 2001to 2007. He is currently an Assistant Professor at theDepartment of Electronic Engineering, the Univer-sity of Electro-Communications. His current researchinterests include computational acoustics, acoustical

electronics, and speech dynamics. He received the Awaya Prize ans theSato Prize from ASJ in 2001 and 2003, respectively. He is a member ofthe Acoustical Society of Japan, the Acoustical Society of America, and theMarine Acoustics Society of Japan.

Tomoo Kamakura (Non-member) received his B. E. degree fromKanazawa University, and M. E. and Dr. E. degreesfrom Nagoya University in 1971, 1973, and 1977,respectively. He was an Associate Professor at theDepartment of Electronics, the University of Electro-Communications from 1985 to 1997. He is cur-rently a Professor at the the University of Electro-Communications. His current research interests in-clude nonlinear acoustics, ultrasonic measurement,and acoust-electronics. He received the Sato Prizes

from ASJ in 2003 and 2006, and received the best paper award from MASJin 2003. He is a member of the Acoustical Society of Japan, the AcousticalSociety of America, the Marine Acoustics Society of Japan, and the Instituteof Electronics, Information and Communication Engineering.

Koji Ueda (Non-member) received B. E. and M. E. degrees in Elec-trical Engineering from Meijo University in 1981 and1988, respectively. Since 1981, he has been workingin Nagoya Electric Works, Co., Ltd. He is currentlya manager of the research and development division.He received Dr. E. degree from Nagoya University in1996. He is a member of the Information Process-ing Society of Japan and the Institute of Electronics,Information and Communication Engineering.

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