Coordinated VANET Experiments -a Methodology and First Results

Markus KerperDriver Information Systems

Volkswagen Group Research, Germanymarkus.kerper@volkswagen.de

Wolfgang Kiess Martin MauveComputer Networks Group

University of Dsseldorf, Germany[kiess,mauve]@cs.uni-duesseldorf.de

ABSTRACTMany applications for inter-vehicular communication require a veryspecific test situation in order to be evaluated during a real worldexperiment. However, in the currently prevailing free-flow exper-iments the intended situation for the application may not occur insufficient frequency to provide statistically significant results. Inthis paper, we argue that the existing experimental approach shouldtherefore be complemented with coordinated experiments. This al-lows to specifically create the desired test situation.

We present a methodology for such controlled experiments. Thekey to those experiments is the exact coordination of the participat-ing cars movements as well as a detailed control over all used soft-and hardware. To illustrate this methodology, we have performedan experiment with two cars that simultaneously approach an ur-ban intersection and measured the radio transmission ranges withand without a road side unit. The results show that cars equippedwith communication technology can inform their drivers about theapproaching other cars up to ten seconds before a possible accidentat such an intersection.

Categories and Subject DescriptorsH.4.3 [Information Systems Applications]: Communications Ap-plications; C.2.1 [Computer-Communication Networks]: Net-work Architecture and DesignWireless communication

General TermsDesign, Experimentation, Measurement

KeywordsVANET, Experiment design, Real-World Measurements, Intersec-tion

1. INTRODUCTIONIn Vehicular Ad Hoc NETworks (VANETs), cars equipped with

communication technology exchange information about position,speed, traffic jams or the occupation of parking lots. VANET appli-cations like a cross traffic assistant or emergency vehicle warning

Permission to make digital or hard copies of all or part of this work forpersonal or classroom use is granted without fee provided that copies arenot made or distributed for profit or commercial advantage and that copiesbear this notice and the full citation on the first page. To copy otherwise, torepublish, to post on servers or to redistribute to lists, requires prior specificpermission and/or a fee.VANET09, September 25, 2009, Beijing, China.Copyright 2009 ACM 978-1-60558-737-0/09/09 ...$10.00.

use this information to support or warn the driver in specific situ-ations. It is the purpose of field operational tests to evaluate suchapplications under real-world conditions.

However, in free-flow experiments, where drivers are not givendetailed movement instructions and the used software is not con-trolled, the target situation will only occur by chance. Thus, a free-flow experiment would have to run for a long time at a high cost toget enough situation samples. Furthermore, if the time gap betweenthe successful runs is too big, external effects like changes in trafficor weather conditions are likely to distort the result.

In contrast, we propose to also test such VANET applications incoordinated experiments. Here, drivers are given detailed move-ment instructions while the actions of the application to be testedas well as of all other test software are automated. This allows torepeatedly test the application in the very situation for which it hasbeen designed. In this paper, we define and evaluate a methodologyfor such coordinated experiments in a real-world environment.

In the first part of the paper, we introduce our methodology toplan, conduct and evaluate VANET experiments, specifically ad-dressing the challenges of movement coordination. In the planningphase, movement traces of the participating cars are recorded andserve as basis for planning individual test drive schedules accord-ing to a global scenario schedule. When conducting an experiment,the drivers are instructed with a dedicated experiment navigationsystem, providing detailed visual and acoustic driving instructions.This allows the drivers to adjust the speed to fit the predefinedmovement. During the experiment, the actions of applications andtools are triggered automatically and are regularly monitored froma central computer connected to the cars via UMTS. In the evalua-tion phase, the movement traces of the runs are tested for similaritybefore the communication analysis is conducted.

In the second part of the paper, we apply our methodology tostudy the design parameters of an intersection collision warningassistant in a real-world setting. We determine the basic (commu-nication) characteristics of this driver assistance system in the verysituation for which it has been designed: the orthogonal, simulta-neous approach of two cars at an urban road intersection.

This paper is structured as follows. In Section 2, we discussrelated work. This is followed by a description of our methodologyfor conducting controlled VANET experiments in Section 3. InSection 4, we illustrate the application of our methodology to theevaluation of an intersection collision warning assistant. Section 5concludes the paper.

2. RELATED WORKPlanning and realizing real-world VANET experiments in a co-

ordinated way is challenging. Up to now, such experiments havebeen realized in setups where cars either followed each other or

moved completely freely. An early example for the car-followingapproach with up to five cars is an evaluation of DSR [11]. Anotherexample are the experiments in [12], where ten cars drove loops ona 5-mile freeway segment. In [5], three cars followed each otherto collect experimental data of UDP and TCP throughput in inter-vehicle communication on highways and in suburban areas. As thecars in the given examples were just following each other, it wasvery easy to coordinate their movements.

Jerbi et al. [8] analyzed the performance of multi-hop car-to-car communication and infrastructure-to-car communication. Theexperiments were conducted with stationary cars and cars drivingone behind another. Realizing these experiments the speed of thecars was varied between the runs, but in contrast to our scenariothe cars had not to drive on different routes. Data throughput ininfrastructure-to-car communication was also measured in [3] andin [4], where a single car had to pass an access point repeatedly atpredefined speeds.

Controlled experiments with mobile ad-hoc networks are describedin [9, 10]. The experiments were conducted in a building with hu-mans carrying PDAs on predefined routes. Movements were con-trolled by defining a constant movement speed; the PDAs werecontrolled and supervised via wireless LAN. Obviously, the motionpatterns and speed behavior of cars on public roads is much morecomplex and the larger distances between VANET nodes impedesuch a simple control approach. Therefore, the presented solutionscannot be directly applied in the vehicular context. Nevertheless,it provides a good starting point for our own methodology that isbuild on this work and transfers it to such a VANET scenario. Es-pecially the concept of supervising the nodes with one monitor andthe time schedules to coordinate the different applications and tasksare useful for our experiments.

The most important communication parameter for an intersec-tion assistance application is the communication range. In [1], thishas been studied with a simulation of the worst-case situation withdifferent parameters (e.g. driver type, velocity, max. deceleration).This study shows that a communication range of 120 m is suffi-cient for achieving a timely warning at the full velocity range up to100 km/h. For achieving a timely warning at inner-city velocitiesof 50 km/h, already a communication range of about 75 m is suf-ficient. Furthermore, in [2] an intersection assistant was designedand evaluated with focus on user acceptance. To this end, the au-thors equipped two test cars and measured the influence of the as-sistant on driving behavior in a critical situation. The test resultsshowed that such an assistant can assist the driver and improve traf-fic safety. In contrast to this work that focuses on the analysis ofuser acceptance, we evaluate communication parameters for suchan assistant on public roads.

3. EXPERIMENT METHODOLOGYIn this section, we describe our methodology for conducting con-

trolled real-world experiments with inter-vehicular communication.The methodology is divided into the three different steps of plan-ning, realization and evaluation of the experiment as it is shown inFigure 1.

We propose a new approach to plan car movements by transfer-ring and extending existing work on the control of experiments withgeneral-purpose mobile ad-hoc networks [10] to the VANET con-text. Additionally, we introduce an experiment navigation systemto guide the drivers. Finally, we extend the experiment evaluationby a movement similarity analysis before performing the applica-tion and protocol evaluation.

Figure 1: Steps of the experiment methodology.

3.1 PlanningThe goal of planning car movements as part of a coordinated ex-

periment is to define detailed movement instructions for each par-ticipating car, so that the cars arrive at a specified region at a pre-defined time. An example is the simultaneous arrival of cars at anintersection. To this end, the routes to be followed by the cars are atfirst roughly defined with the help of a map. In a subsequent step,the exact coordinates for these movements can then be determinedby recording GPS positions while driving along these routes.

To set up a scenario like this, several planning steps are required.Initially, the intended route of each car is divided into three sec-tions: approach, region of interest, and departure (shown in Figure2). The most important of these sections is the region of inter-est. It defines the part of the route that we are interested in for anevaluation. Within this region, applications based on inter-vehiclecommunication (or specific details of the communication itself) aretested. In order to create the desired test situation, the participatingcars need to reach the region of interest at a specific point in time.Approach and departure are those parts of the route that connect theregion of interest with the start and end positions of the vehicles.

Figure 2: Experiment planning sections.

We begin by setting the start and end position of each car. Theseshould adhere to the following criteria:

stopping there even for minutes (e.g. because there is a prob-lem at the other car(s) and the experiment is delayed) shouldnot endanger the driver or the surrounding traffic

it must be possible to pull out fast and easily from the startposition to prevent delays

the positions are sufficiently far apart to avoid unwanted net-work connections between the cars before the experimentstarts

The following step is to determine the real coordinates and driv-ing times on the single routes to the region of interest. We callthis exact movement pattern to be followed by the driver ghost

movement. Note that the individual routes are of varying lengthand are composed of a varying number of objects that may inter-fere with the originally planed driving schedule (e.g. traffic lights,crosswalks, and road side parking lots), which must be taken intoaccount in the ghost movement: the higher the number of suchinterfering objects, the higher the variance of the driving times be-tween runs. Therefore the movements on each route need to berecorded repeatedly to get an appropriate number of samples. Forour own setting, two to five samples were sufficient for this.

To select an appropriate ghost movement, the arrival times at theregions of interest are compared. From our experience it is helpfulto exclude movements faster than the average movement, as it iseasier to drive slower than to catch up with the ghost position ifone is behind schedule. Furthermore, the chosen movement has tobe smooth, meaning that wait times at traffic lights or crosswalksshould be comparable to the values in multiple runs. With this, itis likely that similar wait times occur again during the experiment.By this method one ghost movement is selected out of multiplerecorded movements.

After choosing the ghost movement for each single route, thearrival times at the region of interest between the different routesneed to be harmonized. This is necessary as the cars have differentdriving times to this region. For this task, we have written a spe-cial software called CarMovementPlanning. It allows to adjust thestart times of the cars via a graphical user interface and visualizesthe resulting tracks. With this, we then create a global, harmonizedexperiment movement script that contains detailed position infor-mation for each car in relation to the start of the experiment.

3.2 RealizationThe main problem in coordinated real-world VANET experi-

ments is the control of soft- and hardware as well as the coordi-nation of cars. Often, the distances between the participating carswill be too large to coordinate these tasks by walkie talkie instruc-tions. Even if mobiles could be used, the single steps, e.g. startingthe application at the right moment or triggering the tracing toolswith the correct command line parameters are so complex that theycannot be manually executed during driving. Furthermore, as in allexperiments with moving participants, a global guidance systemfor the driver is required. Thus, solutions need to be found to con-trol the experiment from a central position as well as to give usefulinstructions to the drivers.

The experiment control problem has been tackled for mobile ad-hoc networks with pedestrians in the EXC project [9]. For our ownevaluation, we adapt the provided open source experiment controlsoftware EXC and solve the new challenges that arise in experi-ments with cars. EXC automates an experiment as much as possi-ble. A typical EXC experiment setup consists of clients (in our casethe cars) and a monitor (in our case a laptop computer) to controlthe clients. EXC automates the actions of the client software forour field trial application, these are monitoring and controlling ofthe application, communication device, and the driver interface by means of a time based instruction script. In the initial EXC im-plementation, control commands are sent to the clients over a WiFinetwork and executed via remote method invocation.

In experiments with inter-vehicle communication, there is usu-ally no permanent WiFi connectivity due to the large distancesbetween the participating cars. Thus, the exchange of control in-formation via WiFi is not always possible. Instead, we proposeto replace WiFi by a cellular connection using Universal MobileTelecommunications System (UMTS) devices. With this, controlchannel and test network do not interfere, the control channel com-munication is reliable and the distances between the participating

Figure 3: Experiment navigation system.

cars are unimportant. Thus, it is possible to get status informationof all participants between the runs and start the next run from thecentral monitor.

To coordinate the movements of the drivers in our experiment,we have developed a dedicated experiment navigation system. Ascreenshot of its graphical user interface is shown in Figure 3. Itsupports the driver with information about the different positions:the route to be followed is represented by a solid line, a red dotshows the ghost position and the current own GPS-derived positionis indicated by a blue dot. Textual information like the distancebetween the current position and the ghost position are shown atthe bottom of the user interface.

However, as constantly looking at the screen while driving doesnot work well and is dangerous, we also implemented a speechoutput. It informs the driver about the relative distance to the ghostposition and allows him to accelerate and decelerate appropriatelyto be as close to it as possible. Although hearing a voice that givesdetailed position commands during driving was strange at the be-ginning, the drivers accepted it after a short time and then were ableto precisely follow the instructions.

3.3 EvaluationIn experiments, a sound interpretation of the results is gener-

ally only possible on the basis of data collected in a sufficientlylarge number of similar, repeated runs. Evaluation of inter-vehiclecommunication has traditionally focused more on the analysis ofrecorded communication data than on the occurring vehicular move-ment patterns as the employed simple car movement patterns couldeasily be repeated [3]. Evaluation of real-world car movements be-comes important when coordinated experiments are more complexand are conducted in normal road traffic environments. Here, itmay not be possible for the drivers to obey the given movementinstructions in each run due to the presence of other cars or pedes-trians. Therefore, we extend the experiment evaluation by the stepof movement analysis.

We use two measures for the comparison of movements, namelysingle route movement similarity and multi route movement similar-ity. While single route movement similarity indicates the similarityof movements by one car on one route in different runs, multi routemovement similarity indicates the similarity in relative movementsof all cars participating in an experiment run.

In general, different experiment runs with multiple cars are sim-ilar if the movements of all participating cars in relation to eachother in these runs are similar. As applications are tested within theregion of interest, the movements in the approach and departuresection of the route can thus be neglected. To further simplify thecalculations (and due to the relatively short region of interest thatwill often only span one radio range), we concentrate on the arrivaltimes at the beginning of this region. With this, assessing similarityboils down to a comparison of the arrival times: we propose to usetheir empirical variance as indicator here.

Furthermore, car movements can also be considered as similarif all cars arrive by the same amount late or early. Thus, similarruns can be identified by only taking the movement offset betweenthe arrival times at the region of interest into consideration. Tocalculate these offsets, we propose to compare the arrival time ofthe first car at this region with the arrival times of other participatingcars at this time.

Having calculated these offsets, the next goal is to identify clus-ters of runs with similar offsets. For this purpose, we propose touse a hierarchical clustering algorithm with a maximum relativedistance between elements of each cluster. The maximum relativedistance for clustering the runs determines the degree of similaritybetween runs in a cluster. To give a specific example, in our ex-periments in an intersection scenario we used a relative distance of20 m and a minimum cluster size of three elements. By this clus-tering, a basis for further analysis is created, so that the evaluationof the communication quality and performance is performed withruns with similar movement patterns.

4. AN INTERSECTION SCENARIOIn this section, we describe how the presented methodology was

applied to a real experiment. The goal of the experiment was toexamine the transmission ranges of two cars that simultaneouslyapproach an intersection on orthogonal streets under regular trafficconditions. This is a crucial design parameter for applications likean intersection assistant: if the transmission range of the communi-cation devices is large enough, drivers may be alerted of a possiblecollision in good time to react accordingly. The experiments nec-essary for this study require a very specific test situation, just thescenario for which our methodology is designed.

As a basis for this experiment, we first describe the hardwareand software setup. Then, we proceed through each step of theexperiment methodology.

4.1 Hardware and Software SetupTo approximate the hardware of future inter-vehicle communica-

tion systems, we use IEEE 802.11a [6] interfaces (frequency 5.7 GHz)for our tests. These are already in their initial configuration verysimilar to the future standard IEEE 802.11p (WAVE) [7] (5.9 GHz)radios: from theory, we know that communication ranges in freespace are almost the same at the frequencies of 5.7 GHz and 5.9 GHz.Furthermore, other radio propagation effects, e.g. reflection and re-fraction are also similar. However, factors like network card outputpower, cable loss and antenna gain play an equally important roleand therefore must be appropriately adjusted and verified.

The experiments were conducted with three Ubiquiti NetworkSR71 Cardbus network interfaces [15]. The datasheet for this in-terface shows an output power of 20 dBm for 802.11a. However, itwas not possible to achieve this in pre-experiment tests. Since theoutput power allowed in IEEE 802.11p is between 30-33 dBm weuse an amplifier to increase the output power of the network card to30 dBm (1000mW). Furthermore, as antenna characteristics have ahuge impact on the experiment results, we measured cable loss and

antenna gain with a spectrum analyzer. The radiation of the anten-nas was also verified as shown in Figure 4. Combining the outputpower of the amplifier as well as all gains and losses, the EIRP1 inour experiments is about 500 mW.

Figure 4: Experimental setup for antenna verification: a turn-ing platform was used to turn the vehicle from 0 to 360 degrees.During this process the signal quality was measured with aspectrum analyzer. The results showed that the experiment an-tennas are radiating with an omnidirectional and regular pat-tern.

As operating system we used Kubuntu Linux in version 8.04and kernel 2.6.24-19 on three Lenovo ThinkPad x61s. The net-work cards ran in ad-hoc mode with the Madwifi [14] v0.9.4 driverwhich was modified to log signal-to-noise ratio (SNR), averageSNR and noise for every received packet. The devices were set-up with static IP-addresses and the same network SSID, so thatsending and receiving broadcast packets was possible without fur-ther association. Each car periodically sent out its own position inhello-messages every 200 ms (in car-to-car communication, thesemessages are generally referred to as beacons). We derived ad-ditional positions by extrapolating the 1 Hz GPS positions. Thepacket size of the beacons was 256 Byte including headers. Theexperiment logging was done with TCPDump [13].

4.2 Planning

Figure 5: Aerial view on the intersection in Wolfsburg, Ger-many where the experiments have been conducted with tworoughly defined movement traces and two stars marking thebeginning of the region of interest.

1Equivalent Isotropic Radiated Power

As described in Section 3.1, the experiment planning starts withthe rough definition of the car movements. The intersection weuse for our test is located in Wolfsburg, Germany, see Figure 5.It is a normal urban intersection with buildings along the sides ofthe roads. The figure also shows an example for a roughly definedmovement for the two cars.

The movement definition is followed by the selection of start andend positions for each car. We defined four positions, one for eachdriving direction towards the intersection. We used these as startas well as end positions, so that each car drives straight across theintersection and is able to use the target of one run as start positionfor the following run. In our setup, the first car drives from West toEast and the second car drives from North to South. In the next runthe directions are reversed so that no time is wasted by switchingpositions.

The start of the region of interest in our experiment is indicatedby a star on the map in Figure 5. In an earlier study [1], it has beendiscovered that the required transmission range to realize an inter-section assistant for urban environments is 75 m. Furthermore, thedistance of the stop lines to the center of the examined intersectionis 20-25 m. As the region of interest marks the part of the routewhere the movements of the cars are more important and will becompared in the evaluation, the regions of interest in our settingstart at a distance of 100 m and end at the center of the intersection.Since the movements before this position and also after the inter-section are not relevant for an intersection safety application, theseneed not be considered in the planning phase.

Once the definition of start and end positions was done, we recordedthe movement data several times and chose a ghost movement foreach direction. For this we had to take into account the speed lim-its and the interfering objects on each respective street. We namedthe four streets Shopping Street, Average Street, Zone 30 and FreeStreet. The maximum velocity on Zone 30 is 30 km/h and on allother roads 50 km/h. The interfering objects located between thedefined start points of the experiments and the intersection centerare as follows:

Shopping Street direction West to East; two traffic lights, twocrosswalks and parking lots on both roadsides.

Average Street direction East to West; one traffic light.

Zone 30 direction North to South; two crosswalks and two priorityto right crossings.

Free Street direction South to North; no obstacles and low trafficdensity.

As last step for planning the movement patterns, we used theCarMovementPlanning tool to adjust the start times of the cars andto create an experiment movement script.

4.3 RealizationThe experiments were realized with the experiment control soft-

ware EXC [9], like described in Section 3.2. The UMTS controlchannel was used to send experiment start commands to the clientsand request system status as well as position information of eachparticipating car. By this approach, we were able to control the ex-periment software and locate any car at any time. Since the run endtime is scheduled in the experiment runscript, this command wasnot send via UMTS.

As outlined above in Section 4.2, we planed two different exper-iment movement scenarios with two cars; Car 1 drives from West toEast and Car 2 drives from North to South in the first scenario (the

directions shown in Figure 5) and reversed directions in the sec-ond scenario. Each direction was driven ten times for the scenariowith and without RSU at the intersection. Thus, the total numberof experimental runs with two controlled cars is 40.

In the following evaluation, we focus on the first pair of drivingdirections for the cases with and without RSU and discuss these re-sults, but also mention the difference between these and the resultsof the second pair of driving directions.

4.4 EvaluationBefore we come to the results of the experiments with two cars,

we first concentrate on the efficiency of the movement instructionsfor the case of a single car.

4.4.1 Efficiency of Movement InstructionsAfter planning a scenario and recording the first movements, we

were interested in how efficient movement instructions are and howexact the movements of cars can be controlled in real-world traf-fic. To answer this, we have performed a number of initial ex-periments with uninstructed movements to compare these with in-structed movements. We apply the proposed approach to analyzethe single and multi car route movements.

During initial free flow tests we discovered that instruction-freeexperiments are not feasible on all routes, since the differences be-tween the arrival times at the region of interest are too large. Es-pecially, the movements on Shopping Street differ a lot as shownin Figure 6(a). Here, the region of interest starting in a distance of100 m to the intersection and the stop lines at the intersection aremarked with vertical lines. The stop lines are marked to show atwhich point the movements are depending on the traffic light statusand cannot be influenced by the driver any more. The maximumdifference on arrival at the region of interest is about 40 seconds,excluding run_10 which failed because of a traffic jam. This runtherefore is excluded from further analysis.

To investigate the potential influence that a driver has over themovement of his car, we realized an additional experiment (notshown here). The goal of this experiment was to investigate whetherthe drivers are able to adjust their speed to fit a predefined move-ment in further experiments with the help of an experiment naviga-tion system. The setup was as follows: in five runs we tried to driveas fast as possible (without breaking the traffic rules), similarly wedid five runs where we tried to drive as slow as possible withoutbothering other vehicles and at a minimum speed of 40 km/h. Re-alizing this experiment, the influence on the movement by acceler-ation and a small speed adjustment is determined.

We discovered that the driver is able to influence the movementssignificantly. The arrival times of the fast runs was lower than theslow runs on three routes. Unfortunately, the movements on Shop-ping Street could not be influenced in all runs by this method. Eventhough it was not possible to influence the movements on this roadas much as on other roads, we still achieved a higher movementsimilarity with the experiment navigation system and by lifting theminimum speed restriction.

In comparison to the uninstructed movements on Shopping Street,Figure 6(b) shows ten mobility traces that have been collected bydriving on this street using the experiment navigation system. Dueto the speed adjustments of the driver, the arrival times at the re-gion of interest are much more similar. A detailed comparison ofthe empirical variances of these arrival times for all routes is shownin Figure 7, reporting results with and without the navigation sys-tem.

As can be seen, the empirical variance of instructed movementsis significantly lower when using the navigation system on the re-

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Figure 6: Details of the experiment comparing free flow andinstructed movements for Shopping Street.

spective street. The variance on Shopping Street is about ten timeslower using the navigation system when compared to uninstructeddriving. Even for the other routes that already display low vari-ance without technical help, the similarity of movements can beimproved with the proposed experiment navigation system.

As mentioned before, these variances are only an indicator forthe similarity of runs on one route, but not for the relative positionof two cars. Because of this, the clustering of the movement offsetof both cars has been computed as described in Section 3.3 and isshown in Figure 8 for the scenario Shopping Street/Zone 30.

The movement offset can be negative or positive; a positive valuemeans that the car on Shopping Street reached the region of interestfirst while a negative offset means that the car on Zone 30 arrivedearlier. With the proposed algorithm for multi route car movementwe formed clusters of at least three runs using a simplified thresholdclustering algorithm to be able to compare the radio characteristicsof these runs. In this case the calculated two clusters are markedby the white and by the gray rectangle. The last three runs wererefused by the reason that their similarity is not sufficient. The dis-tances of the clustered runs are smaller than 20 meters. The resultsfor the runs of the second scenario, Average Street and Free Street,are similar to these. We were also able to apply the clustering algo-rithm and analyze the transmission ranges afterwards.

4.4.2 Transmission Range

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A central goal of this study is to determine the transmission rangeat intersections to provide design parameters for an inter-vehiclecommunication based intersection assistant. In this section, we firstshow the results of the measurements and then compare the trans-mission ranges of different experiment runs.

After the similarity of the movements in this experiment has beenanalyzed, it is then possible to analyze the radio characteristics forthe clustered runs. The first step is finding and visualizing com-munication events. Such an event occurred when a beacon of onecar was received by the other car. In the analysis, we mark such anevent by the pair of distances of both cars to the center of the in-tersection at the time of reception. Since the number of such pairsis very high and the display of every pair would be confusing, theevents within a range of 15 meter are grouped. As an example, thedistances between 60 m and 74 m are shown as 60 m. The com-munication events of two cars approaching the intersection in allten runs on Shopping Street/Zone 30 are shown in Figure 9 to givean overview on occurring communication events. The movementscorresponding to this communication events are shown above inFigure 6(b). Excluded are events that occur after crossing the inter-section as they are not relevant for an intersection assistant.

Because of the experiment design where both cars should reachthe intersection center at the same time, the communication eventsshould occur around a line through the origin. Due to the different

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speed regulations on the roads in our scenario, the travelled dis-tances are not equal and this line will not have a gradient of onebut is a little bit tilted. If the movements would perfectly fit theinstructions all measured communication events should be on theline through the origin. Since this will not be the case in reality,we expect events to be scattered around this line. Communicationevents with both distances smaller than 30 m are likely to be raredue to the fact that the stop lines are about 20-25 meters away fromthe intersection center, so that the possibility of having both carsclose to the point of origin at the same time is not given.

The first communication events in Figure 9 occurred at an aver-age distance of about 120 m (Shopping Street) and 105 m (Zone 30)to the center of the intersection. The reason for this early connec-tion is a gap between the buildings along the side of the road thatallowed for a short line-of-sight connection, see also Figure 5. Theconnection was then interrupted for a few seconds and restarted atabout a distance of 75 m. This range is shown by the grey back-ground in Figure 9.

In the next step, we were interested in the time and position of thefirst beacon exchange, since the first beacon can be used by an in-tersection safety application to analyze whether there are other carsor not and to predict their position. At lower distances, a higherpacket resolution allows to observe the movements of other carsin detail. Thus, we also determined the time after which all subse-quent beacons are received successfully. The positions where thecars received the first packet and where they started receiving bea-cons without any loss are shown in Figure 10 for the runs 4, 8, 9,and 10 (the runs with a high similarity in the movement analysis,see also Figure 8).

This analysis also shows that runs with similar movements aresimilar in communication. Comparing the times of first contact andloss free communication, the maximum difference between the dif-ferent repetitions is 23 m. From the movement analysis, we knowthat the maximum movement difference between these runs is 18 mand is thus almost identical to the communication difference. It cantherefore be concluded that the transmission range is similar if themovements are similar.

Additionally, the same analysis was done for the opposite direc-tion. The results close to the center of the intersection are similar,but the surrounding houses did not allow communication events atdistances larger than about 75 m. Altogether, the results lead to theconclusion that data can be exchanged starting at a radius of about75 m around the intersection center.

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Figure 10: Distance of first contact and interruption free con-nection of runs 4, 8, 9, 10.

4.4.3 Transmission Range with RelayHaving determined the achievable transmission range with two

cars at the intersection, we were interested in determining the pos-sible advantage from supporting communication with a Road SideUnit (RSU) at or near the intersection. In our experiment, we im-plemented the RSU as simple repeater to extend the communicationrange. The repeater was placed at the corner Zone 30 and AverageStreet at the intersection with an antenna height similar to the carheight. The results of this measurement with RSU are shown inFigure 11. Unfortunately, due to a malfunction of the RSU tworuns failed, so that just eight out of ten runs could be conductedsuccessfully.

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Figure 11: Communication events of eight runs with RSU onShopping Street/Zone 30.

As can be seen, the usage of an RSU almost triples the transmis-sion range in comparison to the scenario without RSU. Commu-nication events occurred at a distance of about 190 m and 150 m.The results for the experiment on Average Street and Free Streetare similar, the transmission range was also three times as high aswithout RSU. However, it is not possible to identify a fixed trans-mission range around the intersection from this evaluation like the75 m region from the experiment before. In this experiment a con-nection could obviously be established if both cars are in transmis-sion range of the RSU. It is thus interesting to know the distance atthat the connection to the RSU could be started. Therefore we an-alyzed the time at which the cars received a repeated own messagefrom the RSU for the first time. The results are shown in Table 1.

Run Shopping Street Zone 301 212 m 143 m2 199 m 140 m3 210 m 139 m4 194 m 138 m5 196 m 135 m6 198 m 140 m7 175 m 142 m8 170 m 149 m

Table 1: Distances when the first contact to the RSU occuredwhile approaching the intersection.

These results indicate a transmission range of all runs on Shop-ping Street of about 200 m, in contrast to Zone 30 on which onlyabout 140 m could be achieved. The difference between the twodirections is a result of the antenna placement at the intersection;the line-of-sight for Zone 30 was blocked by a commercial signin this direction. Summarizing this experiment, the transmissionrange could be tripled on Shopping Street and doubled on Zone 30.The results on the other streets showed similar transmission rangesto Shopping Street.

5. CONCLUSIONSIn this work, we have presented the first methodology for plan-

ning, realizing and evaluating coordinated VANET experiments withthe goal to create a desired test situation under real-world condi-tions. The planning phase starts with a rough definition of thecars routes. Following this, detailed movement data is collectedby driving along the planned routes. For each route, one of thosemovement traces is selected as a reference. Using this information,the global starting times for each car participating in the experi-ment is determined. During the experiment, the driver gets visualand acoustic feedback on the difference between target and actualposition in order to be able to adjust the speed accordingly. Thesoftware tools used in the experiment are controlled via the experi-ment control software EXC and are centrally monitored via UMTSlinks. Finally, during the evaluation phase, a movement analysisis used to identify runs with similar mobility patterns. These arelikely to result in similar radio layer behavior.

To evaluate the effectiveness of this approach, the movements ofcars in city traffic and the potential influence of the driver on thismovement has been analyzed. We were able to show that driverscan control their movement with the help of our experiment naviga-tion system sufficiently to realize coordinated experiments on pub-lic roads. The resulting car movements show a much higher simi-larity when compared to uncontrolled movements, and this similar-ity is also reflected in the communication patterns.

We then applied the methodology to an experiment at an urbanintersection to determine the communication parameters of an in-tersection collision warning assistant. The transmission range oftwo cars approaching the intersection orthogonally was determined.We discovered that if both cars have a similar distance to the inter-section and they are inside a radius of 75 m, the exchange of infor-mation is possible. Furthermore, in our experiments no packets gotlost after both cars were within a radius of 60 m from the intersec-tion.

Afterwards we analyzed the benefit of adding a road side unit atthe intersection that rebroadcasts all received beacons. This exper-iment showed that the transmission range can be tripled by usinga road side unit. Thus, cars equipped with an intersection assistant

based on this technology could inform their drivers about orthog-onally approaching cars more than ten seconds before a possibleaccident at the intersection.

6. REFERENCES[1] A. Benmimoun, J. Chen, D. Neunzig, T. Suzuki, and Y. Kato.

Communication-based intersection assistance. In IV 05:Proceedings of the IEEE Intelligent Vehicles Symposium,pages 308312, June 2005.

[2] A. Benmimoun, J. Chen, and T. Suzuki. Design and practicalevaluation of an intersection assistant in real world tests. InIV 07: Proceedings of the IEEE Intelligent VehiclesSymposium, pages 606611, June 2007.

[3] R. Gass, J. Scott, and C. Diot. Measurements of in-motion802.11 networking. In WMCSA 06: Proceedings of the 7thIEEE Workshop on Mobile Computing Systems andApplications, pages 6974, Aug. 2006.

[4] D. Hadaller, S. Keshav, T. Brecht, and S. Agarwal. Vehicularopportunistic communication under the microscope. InMobiSys 07: Proceedings of the 5th InternationalConference on Mobile Systems, Applications, and Services,pages 206219, June 2007.

[5] F. Hui and P. Mohapatra. Experimental characterization ofmulti-hop communications in vehicular ad hoc network. InVANET 05: Proceedings of the 2nd ACM InternationalWorkshop on Vehicular Ad Hoc Networks, Poster Session,pages 8586, Sept. 2005.

[6] Institute of Electrical and Electronics Engineers. IEEEStandard 802.11a, 1999.

[7] Institute of Electrical and Electronics Engineers. IEEEP802.11p/D3.0, Draft Amendment for Wireless Access inVehicular Environments (WAVE), 2007.

[8] M. Jerbi, P. Marlier, and S. M. Senouci. ExperimentalAssessment of V2V and I2V Communications. In MASS 07:Proceedings of the 4th International Conference on MobileAd hoc and Sensor Systems, pages 16, Oct. 2007.

[9] W. Kiess, T. Ogilvie, and M. Mauve. The EXC Toolkit forReal-World Experiments with Wireless Multihop Networks.In EXPONWIRELESS 08: Proceeding of the 3rd Workshopon Advanced Experimental Activities on Wireless Networksand Systems, June 2008.

[10] W. Kiess, A. Tarp, and M. Mauve. On the TopologicalRepeatability of Experiments with Wireless MultihopNetworks. In MSWiM 08: Proceedings of the 11thACM/IEEE International Conference on Modeling, Analysisand Simulation of Wireless and Mobile Systems, pages303308, Oct. 2008.

[11] D. A. Maltz, J. Broch, and D. B. Johnson. Experiencesdesigning and building a multi-hop wireless ad hoc networktestbed. Technical Report CMU-CS-99-116, School ofComputer Science, Carnegie Mellon University, 1999.

[12] K. Seada. Insights from a freeway car-to-car real-worldexperiment. In WiNTECH 08: Proceedings of the 3rd ACMinternational workshop on Wireless network testbeds,experimental evaluation and characterization, pages 4956,2008.

[13] Tcpdump: a tool for network monitoring.http://www.tcpdump.org.

[14] The Madwifi driver. http://www.madwifi.org.[15] Ubiquiti Networks Inc. Datasheet: Ubiquiti super range

cardbus. http://www.ubnt.com/downloads/src_datasheet.pdf.

http://www.tcpdump.orghttp://\discretionary {-}{}{}www.\discretionary {-}{}{}madwifi.\discretionary {-}{}{}org

1 Introduction2 Related Work3 Experiment Methodology3.1 Planning3.2 Realization3.3 Evaluation

4 An intersection scenario4.1 Hardware and Software Setup4.2 Planning4.3 Realization4.4 Evaluation4.4.1 Efficiency of Movement Instructions4.4.2 Transmission Range4.4.3 Transmission Range with Relay

5 Conclusions6 References