issn 1751-956x vehicle infrastructure integration system...

Published in IET Intelligent Transport SystemsReceived on 30th January 2009Revised on 23rd November 2009doi: 10.1049/iet-its.2009.0021

ISSN 1751-956X

Vehicle infrastructure integration system using visionsensors to prevent accidents in traffic flowK. Fujimura T. Konoma S. KamijoInstitute of Industrial Science the University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo153-8505, JapanE-mail: [email protected]

Abstract: This study describes the development of a vision sensor for detecting shock waves which is one of the main factors ofaccidents in highway traffic flow. The major contributions of this research are development of vehicle tracking and detection ofshock wave in saturated traffic. Moreover, realisation of a vehicle infrastructure integration (VII) system for providing arrivalinformation of such propagation to drivers is proposed. The experiment on the analysis of the propagation with the developedimage sensor has shown that an error might occur in the arrival time information of the propagation provided to the driver.Therefore a prediction technique at the arrival time of the propagation is integrated in the authors’ system. By using thisprediction technique and taking the error tolerance of drivers into account, the experimental results show that predictionsuccess rates are improved by about 5%.

1 Introduction

Fatalities from traffic accidents in Japan are graduallydecreasing owing to legal measures such as making the useof seat belts compulsory and the development of emergencymedical care. On the other hand, the number of accidentshas increased [1], and so the improvement of road safety isrequired to be considered with special attention. In thiscontext, many incident detection systems have beendeveloped [2–6]. Recently, many driver assistance systemsfor incident detection as well as prevention have beendeveloped worldwide. These are mainly road and vehiclecooperated-type systems such as the vehicle infrastructureintegration (VII) project in the USA, SARETEA project inEurope and advanced cruise assist highway system (AHS)project in Japan. Among them, in the California Path, fieldtests of collision warning systems in intersections [7, 8] and,in Japan, field tests of collision warning systems for obstacleforward in Tokyo metropolitan expressway [9] have beencarried out. However, it is learnt that much research workremains to be done in these areas. So, this paper willexplore, examine and extend the current VII systems.

Real-time traffic measurement systems are important forthe driving assistance systems. Various sensors have beenresearched for the system [10–13]. Above all, a visionsensor is superior to other sensors especially from theviewpoint of cost effectiveness, because the surveillancecamera that has already been set up by the monitoringpurpose can be used together as a vision sensor for thetraffic flow measurement.

In this way, the driver assistance systems and the trafficmonitoring systems have been effective. However, researchon such systems often fails to grasp the factorial analyses oftraffic accidents. Especially in Japan, shock wave, caused by

the repetition of a rapid acceleration and deceleration change,is paid attention to as a particular accident factor in thesaturated traffic flow. The shock wave is a complicatedphenomenon compared with the end-of-queue situationresulting from, for example, single-car accident or traffic jamthat has been addressed by conventional driving assistancesystems such as those described in [9]. Therefore we haveanalysed data of traffic accidents that occurred at theAkasaka tunnel in the Tokyo metropolitan expressway whichis a typical famous point where the shock wave in saturatedtraffic occurs frequently [14]. For the analysis, the incidentdetection system, using both vision sensor and ultrasonicwave sensor that we have proposed, was employed [6]. As aresult, it has been clearly found that the propagation of shockwave generated at downstream bottleneck is mainly the causeof accident. So, in this paper, a vision sensor that allowsdetection of the propagation of shock waves will bedeveloped, and a detailed analysis of the propagation usingthese sensors will be presented. In addition, a VII system thatinforms the propagation to drivers will be proposed.

2 Relational works

2.1 Vehicle tracking algorism

The number of approaches to vehicle tracking has recentlygrown at a tremendous rate. Among them, a vehicle shapemodel consisting of horizontal and vertical edges, byKolling and Nagel [15, 16], is successful and widely used.A boundary extraction method by Peterfreund [17], Kasset al. [18], and statistical analysis and clustering of opticalflows and vector quantisation algorithms by Smith andBrady [19] and Stauffer and Grimson [20] also havesignificant contributions to vehicle tracking research.

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However, these approaches have not taken the occlusioncases into account. In this aspect, Leuck and Nagel [21]and Gardner and Lawton [22] employed 3D models ofvehicle shape and developed a dedicated pattern matchingalgorithm for vehicle images. This method requires variouskinds of vehicle models. So it does not have much impacton applications to a scene containing various kinds ofvehicle travelling in various directions. On the other hand, adedicated reasoning method of Weber and Malik [23]achieved good performance in a sparse traffic with onlysimple motion.

2.2 Traffic monitoring system using vision sensors

There are substantial amounts of recognised image sensor-based incident detection systems. The most widely deployedone is that of the successfully integrating vision sensors andreasoning components into a well known prototype forCalifornia Roads [24]. In similar fashion, Cucchara et al.[25] applied traffic rule-based reasoning to images for simpletraffic conditions on a single-lane straight road. Jung et al.[26] tracked vehicle from traffic images for precise trafficmonitoring. Zhang and Taylor [27], Srinivasan et al. [28],and Byun et al. [29] employed Bayesian network and neuralnetwork to differentiate incidents from ordinary traffics.However, because of the lack of consideration on occlusioncases, these systems have difficulties in being applied tocomplicated traffic situations such as congestion.

3 Overview of the VII system by visionsensor network

In this section, a result of a factor analysis for traffic accidentsin the Akasaka tunnel is represented. This becomes amotivation of our research.

3.1 Factor analysis of traffic accidents in theAkasaka tunnel

We have circumstantially analysed traffic accidents in theAkasaka tunnel by data from both ultrasonic wave sensorsand video cameras installed in the tunnel. They arepractically used for surveillance by the traffic managementofficers. According to the results, these traffic accidents canclearly be categorised into the following two types:

† In the boundary of shock waves: In saturated traffic,vehicles do not move at constant speed. They repeat highand low speed over a period of time. When the trafficdensity is low, vehicles run at 40 km/h. In contrast, whenthe traffic density is high, vehicles are almost stalled. In thissituation, rapid speed difference is caused between low- andhigh-density parts. Therefore rear-end accidents caused byspeed differential are more likely to occur.† In traffic jam: In this situation, incidents occur at a lowspeed about 10 km/h. It would appear that the driver’scarelessness causes these incidents.

From the analysis, it was clear that 70% of these accidentsoriginates in the speed differential in saturated traffic, andthe remaining 30% occurs when a driver goes at a lowspeed without attention. In this paper, the system to preventthe accident of the former type with high possibility ofcausing a serious accident is constructed. It is mentionedthat such a situation is not typical only in the Akasakatunnel, but is also noticed as a common factor of traffic

accidents in some places [30]. Therefore this solution isthought to be effective on any road where saturated trafficoccurs. Also note that this analysis is biased towards shockwave detection. In general, there are many other factorsleading to traffic accidents [31, 32].

3.2 Proposal of the VII system to prevent accidentsoccurring in saturated traffic

Fig. 1 is an illustration of Shinjuku Route of TokyoMetropolitan Expressway. Fig. 1a indicates the installationof ultrasonic wave sensors and Fig. 1b represents thesurveillance video cameras that are installed in the Akasakatunnel every 70–80 m. Then we use these surveillancecameras as vision sensors, and propose a VII system thatdetects shock waves in saturated traffic and provideswarning information to drivers if necessary. This system,however, works not only in tunnels but also in any roadwaywhere saturated traffic occurs.

An overview of the VII system that we propose is shown inFig. 2. The system consists of three parts: vehicle trackingpart, detection part and information providing part. First, anaverage velocity of traffic flow is calculated from a result ofthe vehicle tracking part in a vision sensor. Second, in thedetection part, the incoming shock wave is detected in eachvision sensor by an algorism based on the calculatedaverage velocity. Finally, if the shock wave is detected, awarning information is provided by dedicated short-rangecommunications (DSRC) placed at downstream point of thetraffic flow. The information is related to the amount oftime that drivers take for encountering the shock wave. Thistime is calculated from the position where the shock waveexists at the time the drivers goes through the DSRC spot.The calculation is described in a later section. By thisinformation, drivers are called to attention, and traffic safetywill be improved. In addition, if the shock wave has alreadycome in the downstream point of traffic flow, theinformation is constrained.

In our VII system, both a position detecting the shock waveand a position where drivers encounter the shock wave areimportant. To clear these positions, a detailed analysis ofshock waves is required. However, it is difficult for ultrasonicwave sensors, because the granularity of sensing is rough.Even if surveillance video cameras are installed at manypoints in the Akasaka tunnel, it is also difficult to analyse thepropagation of shock waves by visual check. Therefore inthis paper, a vision sensor that automatically enables thedetection of shock waves is presented in Section 5, by usingthe vehicle tracking algorism presented in Section 4. By thedeveloped vision sensor, the propagation of shock waves inthe Akasaka tunnel is analysed in detail and our VII systemis also discussed based on the result in Section 6.

4 Vehicle tracking algorism

In our VII system, surveillance video cameras that havealready been installed in the Akasaka tunnel are utilised forvision sensors to observe a condition of traffic flow. Thissection describes the detail of vehicle tracking by theSpatio-Temporal Markov Random Field model (the S-TMRF model) [33, 34].

4.1 S-T MRF model

The principal idea of the S-T MRF model is that segmentationof the object region in the spatio-temporal image is equivalent

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to tracking the object against occlusions (see Fig. 3). Usually,the spatial MRF segments an image pixel by pixel. However,since the usual video cameras do not have such high framerates, objects typically move ten or 20 pixels amongconsecutive image frames. Therefore neighbouring pixelswithin a cubic clique will never correlate in terms ofintensities or labelling. Consequently, we defined our S-TMRF model so as to divide an image into blocks as a groupof pixels, and to optimise the labelling of such blocks byreferring to the texture and labelling correlations amongthem, in combination with their motion vectors. Combinedwith a stochastic relaxation method, our S-T MRFoptimises object boundaries precisely, even when seriousocclusions occur. Here, a block corresponds to a site in theS-T MRF, and only the blocks that have different texturesfrom the background image are labelled as part of the

Fig. 2 Flow of a driving assistance system for shock waves by the vision sensor

Fig. 1 Sensors on the Akasaka tunnel in Shinjuku route of the Tokyo Metropolitan Expressway

a Ultrasonic wave sensorsb Vision sensors

Fig. 3 Segmentation of spatio-temporal images


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object regions. In this paper, an image consists of 640 × 480pixels and a block consists of 8 × 8 pixels; such a distributionmap of labels for blocks is referred to as an object-map.

4.1.1 MAP estimation for segmentation: Our S-TMRF estimates the current object-map (a distribution oflabels for object regions) according to a previous object-map, and previous and current images. Here are the notations:

† G(t 2 1) ¼ g, G(t) ¼ h: An image G at time t 2 1 has avalue g, and G at time t has a value h. At each pixel, thiscondition is described as G(t 2 1; i, j) ¼ g(i, j),G(t; i, j) ¼h(i, j).† X(t 2 1) ¼ x, X(t) ¼ y: An object-map X at time t 2 1 isestimated to have a label distribution as x, and X at time t isestimated to have a label distribution as y. At each block,this condition is described as Xk(t 2 1) ¼ xk, Xk(t) ¼ yk,where k is a block number.† V (t 2 1; t) ¼ v: A motion-vector-map V from time t 2 1 tot is estimated with respect to each block. At each block, thiscondition is described as Vk(t 2 1; t) ¼ vk, where k is ablock number.

By the S-T MRF model, we should simultaneously determineobject-map X(t) ¼ y and motion-vector-map V (t 2 1; t) ¼ v,which should give maximum a posteriori probability (MAP)when given the previous image G(t 2 1) ¼ g, current imageG(t) ¼ h and previous object-map X(t 2 1) ¼ x. An aposteriori probability can be described using the followingBayesian equation

P(X (t) = y, V (t − 1; t) = v|G(t − 1) = g,

X (t − 1) = x, G(t) = h) = P(G(t − 1 = g, X (t − 1) = x,

G(t = h|X (t) = y, V (t − 1; t) = v)

P(X (t) = y, V (t − 1; t) = v)/P(G(t − 1) = g,

X (t − 1) = x, G(t) = h)

(1)

Here, P (G(t 2 1) ¼ g, X(t 2 1) ¼ x, G(t) ¼ h), that is aprobability to be given the previous image as G(t 2 1) ¼ g,current image as G(t) ¼ h and previous object-map asX(t 2 1) ¼ x can be considered as a constant. Consequently,maximising the a posteriori probability is equivalent tomaximising

P(G(t − 1) = g, X (t − 1) = x, G(t) = h|X (t) = y,

V (t − 1; t) = v) · P(X (t) = y, V (t − 1; t) = v)

In function (1), P (G(t 2 1) ¼ g, X(t 2 1) ¼ x,G(t) ¼ h|X(t) ¼ y, V (t 2 1; t) ¼ v) represents theprobability along the temporal axis that G(t 2 1) ¼ g,X(t 2 1) ¼ x, and G(t) ¼ h were obtained when X(t) ¼ yand V (t 2 1; t) ¼ v are supposed to be obtained.P (X(t) ¼ y, V (t 2 1; t) ¼ v) represents the probability in thespatial plane that X(t) ¼ y and V (t 2 1; t) ¼ v are supposedto be obtained.

4.1.2 Parameters for correlation function along thetemporal axis: In this subsection, we would like to definethe probability P(G(t − 1) = g, X (t − 1) = x, G(t) =h|X (t) = y, V (t − 1; t) = v) along the temporal axis as the

following Boltzmann distribution

P(G(t − 1)

= g, X (t − 1) = x, G(t) = h|X (t) = y, V (t − 1; t) = v)

=∏

k

exp[−Utemp(Dxyk· Mxyk

)]/ZDk Mk

=∏

k

exp[−UD(Dxyk)]/ZDk

×∏

k

exp[−UM (Mxyk)]/ZMk

=∏

k

exp − 1

2s2Mxy

(Mxyk− mMxy

)2

[ ]/ZMk

×∏

k

exp − 1

2s2Dxy

(Dxyk− mMxy

)2

[ ]/ZDk

(2)

At first, a motion vector with respect to each block isestimated between the previous image and current image bythe block matching technique. By referring to the motionvector, the two S-T MRF energies should be evaluated asshown in function (3)

Utemp(Dxyk· Mxyk

) = b(Mxyk− mMxy

) + c(Dxyk− mDxy

)2 (3)

Dxyk=

∑0≤di,8,0≤dj,8

|G(t; i + di, j + dj)

− G(t − 1; i + di − vmi, j + dj − vmj)| (4)

The first parameter Dxykrepresents texture correlation

between G(t 2 1) and G(t). Suppose Ck is translatedbackwards in the image G(t 2 1) referring to the estimatedmotion vector −VOm

= ( −vmi, −vmj). The texturecorrelation at block Ck is evaluated as (see Fig. 4b) follows:UD (Dxyk

) takes maximum value at Dxyk= 0. The smaller

the Dxyk, the more likely that Ck belongs to the object. That

is, the smaller the UD(Dxyk), the more likely that Ck belongs

to the object.The second parameter Mxyk

is a goodness measure of theprevious object-map X(t 2 1) ¼ x under a currentlyassumed object-map X(t) ¼ y. Assume that block Ck has anobject label Om in the current object-map X(t), and Ck isshifted backwards in the amount of estimated motionvector, −VOm

= ( −vmi, −vmj) of the object Om, in theprevious image (Fig. 4a). Then the degree of overlapping isestimated as Mxyk

: the number of overlapping pixels of theblocks labelled as the same object. The more theoverlapping pixels, the more likely a block Ck belongs tothe object. The maximum number is mMxy

= 64, and theenergy function UM (Mxyk

) takes a minimum value atMxyk

= 64 and a maximum value at Mxyk= 0.

4.1.3 Parameters for correlation function on thespatial plane: In this subsection, we would like to definethe probability P (X(t) ¼ y, V (t 2 1; t) ¼ v) on the spatial



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plane as the following Boltzmann distribution

P(X (t) = y, V (t − 1; t) = v)

=∏

k

exp[ −Uspace(Nyk, Vxk

)]/ZNk Vk

=∏

k

exp[ −UN (Nyk)]/ZNk

·∏

k

exp[ −U (Vxk)]/ZVk

=∏

k

exp − 1

2s2Ny

(Nyk− mNy

)2

[ ]/ZNk

×∏

k

exp[ −UV (Vxk)]/ZVk

(5)

The third parameter of S-T MRF energy is of the neighbourcondition about the object-map of the current spatial planeas shown in function (6)

UN (Nyk) = a(Nyk

− mNy)2 (6)

Here, Nykis the number of neighbour blocks of block Ck that

belongs to the same object as Ck, as shown in Fig. 4c.Namely, the more neighbour blocks that have the sameobject label, the more likely the block is to have the objectlabel. Currently, it is assumed that mNy

= 8, becauseUN (Nyk

) should have minimum value when block Ck andall its neighbours have the same object label. Therefore theenergy function UN (Nyk

) takes a minimum value at Nyk= 8

and a maximum value at Nyk= 0.

Finally, some of such estimated motion vectors would haveerrors. Such errors sometimes occur in blocks whereboundaries of objects exist, where very poor textures existand where some periodical textures exist. Since those errorsshould lead to segmentation errors, it is necessary to correcterrors in motion vectors. For that purpose, it would beeffective to optimise motion vectors themselves by referringto motion vectors of their neighbour blocks on the currentspatial plane as shown in Fig. 4d. Therefore as the fourthparameter, we would like to define the following energyfunction

UV (Vxk) = f

∑Cneighbours

| VCk (t−1;t)

− VCneighbours(t−1;t)

|2/Nxk(7)

Here, Cneighbours represents the neighbour blocks that havethe same object label as the block Ck(t 2 1) and Nxkrepresents the number of blocks of Cneighbours, same objectlabel as the block Ck(t 2 1). Dividing by Nxk

meansnormalisation of the energy by the number of neighbourblocks belonging to the same object. The more a motionvector of a block Ck(t 2 1) becomes similar to the motionvectors of neighbour blocks Cneighbours(t 2 1), the moreprobable a motion vector of a block Ck(t 2 1) becomes.

5 Detection of shock waves by the visionsensor network

This section shows the development of a vision sensor basedon the vehicle tracking mentioned in the foregoing section.First, the measurement of traffic flow is shown. Second, adetection algorism based on the measurement is

Fig. 4 Component techniques of the S-T MRF

a Neighbour condition between consecutive imagesb Texture matchingc Eight neighbour blocksd Motion vectors among neighbours


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represented. Finally, the VII system by a vision sensornetwork is described in detail.

5.1 Detection algorism

Detection is handled with the use of the average speed ofvehicles that pass the vision sensor. The speed of vehiclesis calculated by tracking the results of the S-T MRF model(Fig. 5). In this regard, the horizontal position of a vehicleis defined as the lower side of the frame of tracking results,and both the position on the image and the real position arecoordinated by the calibration. An algorism of the detectionis presented below in detail. The value of n is framenumber per 0.1 s. In addition, the sensor range for trackingthe vehicle is about 70 m towards the vanishing point of theimage. However, the range of calculating the average speedin traffic flow is set to 30 m from the restriction of theresolution

scoren =1 average velocity ≥ Vflow (km/h)2 Vcong , average velocity , Vflow

3 average velocity ≤ Vcong

⎧⎨⎩ (8)

average score =∑n

i=n−128

scorei

( )/128 (9)

conditionn =

flow average score ≤ Scoreflow

critical Scoreflow , average score, Scorecong

congestion average score ≥ Scorecong

⎧⎪⎪⎨⎪⎪⎩

(10)

shockwave detectionn

=

on conditionn = congestion

on conditionn = critical and conditionn−1

= congestion

off conditionn = flow (11)

⎧⎪⎪⎪⎨⎪⎪⎪⎩

† Step 1: Score in each frame is decided by the average speedcalculated by tracking results of S-T MRF model. The score isa value from 1 to 3.† Step 2: The average score is calculated from scores of past128 frames (12.8 s).† Step 3: Traffic conditions in the present frame areestimated {flow, critical, congestion} by the average score.† Step 4: The propagation of shock waves is detected if thecurrent traffic condition is ‘congestion’ or ‘critical’, when thelast traffic condition is ‘congestion’. In this algorism, the‘critical’ condition has a role of preventing unstablechanging of traffic conditions.

5.2 Parameters setting of shock wave detection

The parameters such as Vflow and Vcong in the shock wavedetection algorism are decided based on the factorialanalysis of incidents that occurred in the Akasaka tunnel.This area is one of the high-accident prone locations asmentioned in Section 2. Especially, accident by the shockwave in saturated traffic has become a huge issue. We haveobtained about 150 incidents’ data in the past years andhave analysed the traffic stream of the accidents by theshock wave with ultrasonic wave sensor data. From theresults, the accident by shock waves occurs when the trafficflow changes from a critical to the congestion in saturatedtraffic. To be concrete, the accidents by shock waves occurwhen the velocity of traffic flow is transited from 30 or

Fig. 5 Examples of vehicle tracking results



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40 km/h to around 20 or 10 km/h. From these results, theVflow and Vcong in our algorism are decided to be 40 and20 km/h, respectively. Moreover, Scoreflow and Scorecong

are also decided to be 1.6 and 2.4, respectively. The detailof the traffic analysis of the shock wave is shown in [14,30, 35].

5.3 Shock wave detection by the vision sensornetwork

Both the vehicle tracking technique and the shock wavedetection algorism mentioned above allow the realisation ofthe vision sensor. In the VII system, this vision sensor isimplemented to eight industrial television (ITV)surveillance cameras in the Akasaka tunnel, and a visionsensor network is constructed. The vision sensor networkcan detect the shock wave that occurred in the Akasakatunnel. Then, the result of the detection from eight visionsensors is converted to time information defined astinformation, that is, the time until the shock wave arrives, byexpression (12)

tinfomation = xdistance

Vtunnel

+ tcorrection (12)

In the expression, xdistance is a distance from the DSRC pointwhere information is provided to the vision sensor pointwhere the shock wave is detected. As for the distance

value, the distance from the DSRC point to the entrance ofthe Akasaka tunnel is 260 m, as shown in Fig. 3b, and thevision sensors are arranged at equal intervals in theAkasaka tunnel whose length is 560 m. In addition, theaverage velocity of traffic flow in the Akasaka tunneldefined as Vtunnel is assumed to be 60 km/h. This isobtained from real traffic flow data in the Akasaka tunnel.Then the information is provided to the drivers going in theDSRC spot. tcorrection is a value to correct the value oftinformation. In our system, if drivers do not think tinformation

to be accurate, the reliability of the system to the drivermight decrease remarkably. Therefore the important point tonote is that how accurate time is provided to the driver. Thetcorrection is the value that determines the reliability of thesystem. The tcorrection is discussed in detail in the latter section.

6 Experimental results

To show the effectiveness of our VII system mentionedabove, experiments performed on vehicle tracking precision,shock wave detection precision and optimisation of thesystem are discussed in this section.

6.1 Vehicle tracking

Experiments were performed by using images fromsurveillance video cameras that are installed in the Akasakatunnel. They consist of straight, separating and merging

Fig. 6 Example of shock wave detection by vision sensors


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traffic. Performance for vehicle tracking was evaluated byusing 40 min images at each location by applying the S-TMRF model.

Fig. 6 shows an example of vehicle tracking results in eachvision sensors. In these images, the number of areas includingonly one vehicle is 4185 and these areas were trackedsuccessfully. On the other hand, as for the performance forvehicle segmentation, the number of areas containing morethan one vehicle is 1266 in total. Among them, the numberof areas that were correctly segmented by the S-T MRFmodel is 1181 (about 93% of areas were divided preciselyand tracked successfully). As a result, the vehicle trackingsuccess rate on the whole became 91%. This is the valuethat can be worthy of practical use.

6.2 Detection of shock wave

To evaluate the performance of the developed vision sensormentioned above, experiment of the detection algorithm isconducted. Image data used in experiment are selectedrandomly, but these are in specific conditions as shown inTable 1. The correct answer for the shock wave is obtainedby visual observation. Evaluation indexes in the table aredefined as follows:

† The number of shock waves: count of shock waves byvisual observation.† Correct: count of shock waves observed both in visual andthe system.

† Lack: count of shock waves not observed in the system,whereas it was observed in visual.† False: count of shock waves observed wrong in the system,whereas it was not observed in visual.

As a result of experiment, there are no recall reports in anycondition. Moreover, false reports were not looked even inboth midnight and rainy conditions. The cause of falsereport in fine condition is a failure of cancelling the reportwhen two shock waves are concatenated.

6.3 Optimisation of the proposed VII system

In the VII system to suggest, precision of the prediction timeis important from the viewpoint of reliability. In this section,experimental result of the prediction precision of this time isrepresented.

To investigate the arrival time to a shock wave, a detailedanalysis of shock waves is performed with vision sensornetwork installed in the Akasaka tunnel. The analysed datarelate to 7662 vehicles that passed a DSRC spot between22:00 and 7:00 during 2 days in November 2007. Theresult of the analysis is shown in Fig. 7. Then the numberof drivers who actually encountered a shock wave aftereach vision sensor detected the shock wave as shown inFig. 8 according to a sensor position where driversencountered shock waves. These results indicate that adifference may occur between the position where a shockwave exists when a driver passes a DSRC spot and theposition where the driver encounters a shock wave. This isoriginated because a shock wave propagates from trafficflow of the upstream side to traffic flow of the downstreamside. In addition, the degree of difference is increased in thecondition that the position of the shock wave is far from theDSRC spot and a driver passes DSRC. In the VII system,an error in the calculation of the prediction time iscaused by this difference of position. This time error tendsto occur in the direction that the shock wave arrives earlierthan a driver thought. Therefore it is considered thatcorrection of this error is required when the system predictsthe arrival time.

Table 1 Shock wave detection success rate of the vision sensor

Number of shock

wave

Result of detection Vision condition

Correct Lack False

10 10 0 2 fine (daytime � night)

0 0 0 0 midnight

61 61 0 0 rainy there is waterhole

7 7 0 0 fine (night � daytime)

10 10 0 0 entrance of tunnel

Fig. 7 Shock wave propagation



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So, let us attempt to correct an error by taking advantage ofa correction straight line as shown in line (ii) of Fig. 7 in theprediction time of shock wave arrival. In this figure, line (i)represents the case where the prediction time of the arrivalis not corrected.

To quantify the performance of the arrival time errorcorrection, the prediction success rate is defined for anevaluation index as follows

prediction success rate = Nsuccess

Ntotal

(13)

In the formulation, Ntotal is the number of all drivers (7662)and Nsuccess is the number of drivers of the case that anerror of arrival time is within an allowance (defined asttolerance). Fig. 9 shows the prediction success rate of case ofboth straight line (i) and straight line (ii) for differentttolerance. It is observed that the prediction success rate incase of line (ii) where error is corrected is higher than thatof line (i). When ttolerance is supposed to be degree for5–10 s, it seems that there is effectiveness in the error

correction using line (ii), because the difference of theprediction success rate becomes about 5%.

However, as for the assumption that the x is 5–10 secondsdegree, an investigation from human-factors engineering isnecessary for verification. We have experimentallyexamined the driver’s behaviour using a driving simulatorfor the optimisation of the VII system. Therefore as a futurework, we are going to perform experiments related to howmuch an error will be tolerated by drivers.

7 Conclusions

In this paper, vehicle tracking and detecting shock waves insaturated traffic algorithms have been developed from thepoint of view that the propagation is caused by downstreambottleneck in traffic flow, which is one of main factors fortraffic accident in the critical flow. In addition, a VII systemthat informs arrival of such shock waves to drivers has beeninvestigated by the vision sensor network. To increase thereliability of the VII system, a technique to correct an errorabout a prediction of the shock wave arrival time has beenproposed. By the detailed analysis of the propagation ofshock waves by the vision sensor network in our system,this technique achieves around 5% prediction success rateimprovement at the maximum compared to the case withouterror correction.

In our system, tolerance of prediction time error is animportant factor in determining the prediction success rate.Therefore, we are going to evaluate the tolerance from theview of human-factors engineering in the future.

8 References

1 ‘Japan statistical yearbook 2009’ (Japan, 2009)2 Traficon web site, http://traficon.com3 Hasegawa, E., Onda, M., Kazuno, Y., Kamijo, S.: ‘Development of

traffic obstacles detection system on urban tunnels with heavy trafficflow’. 12th World Congress on ITS, San Francisco, November 2005

Fig. 8 Distribution of the number of vehicles according to the encounter position to shock waves in cases of shock wave position when driverspass the DSRC spot

a akss1b aksk2c aksk3d aksk4e akss5f aksk6g aksk7h aksk8

Fig. 9 Prediction success rate of the system


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