A Complete Simulator Architecture for Inter-vehicle Communication BasedIntersection Warning Systems

A. Avila1, G. Korkmaz13, Y. Liu1, H. Teh1,E. Ekici1, F. Ozguner1, U. Ozguner1, K. Redmill1, O. Takeshita1,

K. Tokuda2, M. Hamaguchi2, S. Nakabayashi2, H. Tsutsui2

Abstract— This paper presents the complete simulator ar-chitecture of the Intersection Warning System (IWS) capableof evaluating different warning systems and communicationprotocols. In this simulator, the network layer, the physicallayer, the driver behavior, and the vehicle traffic are modeledand simulated in detail. An optional repeater is utilized at theintersection to disseminate the position, the velocity, and theacceleration information of the approaching vehicles to the roadsegments shadowed by buildings. The simulation results showthat IWS can significantly reduce both the vehicle collisionpercentage and the vehicle collision speed.

I. INTRODUCTION

The evolution and general availability of inexpensiveWLAN equipment and worldwide progress towards the ac-ceptance of a DSRC standard fuel the design and implemen-tation efforts for Inter-vehicle Communication (IVC) systemsin ITS applications. Intersection collision warning systemsare one type of ITS applications that can prevent collisionsor at least decrease their severity. By exchanging necessaryinformation such as position, velocity, and acceleration, on-board systems can compute the possibility of a collision andwarn drivers. This paper presents the complete architectureof the Intersection Warning System (IWS) simulator whichis capable of evaluating different warning systems and com-munication protocols.

The IWS simulator was developed by OSU during thesecond phase of an ongoing project with OKI ElectricIndustry Co. In the first phase of this project, a WirelessSimulator (WS) was developed to evaluate the performanceof communication protocols in an intersection environment[1], [2]. In this second phase, ITS components (vehicle trafficsimulator, collision warning system, and driver behaviormodel) and wireless communication components are mergedtogether as shown in Figure 1. In addition, the physicallayer model of WS is improved to simulate intersectionswith or without buildings and the model is tuned to fit thefield test results collected in Columbus. Finally, an optionalrepeater is included at the intersection in order to disseminatemessages to the shadowed portions of the road segments. Theevaluation of the collision warning system from the ITS pointof view is given in [3].

1Department of Electrical and Computer Engineering, The Ohio StateUniversity, Columbus, OH, USA

2OKI Electric Industry Co., Japan3Contact Author: Gokhan Korkmaz korkmazg@ece.osu.edu

VTSSimulationParameters

VTS

Driver'sBehavior

Model

Online Wireless Simulator

IntersectionWarningSystem

MessageGenerator

Real timeProcessing

Offline SimulatorOutput

(Extracted)

Physical Layer Model

Fig. 1. Intersection Warning System Simulator Architecture

The remainder of this paper is organized as follows. Thephysical layer model is given in Section II. Section IIIpresents the wireless simulator. Section IV discusses theITS components, namely the vehicle traffic simulator, thedriver behavior model, and the collision warning system.The simulation environment is presented in Section V. InSection VI, the performance evaluation of IWS is discussed.Finally, Section VII concludes the paper.

II. PHYSICAL LAYER

The physical layer component (PHY) in the IWS simulatorserves two major functions: to provide the received powerand the received word error probability at each receivingvehicle. In this section, PHY design as well as differentwireless channel models considered in the IWS simulatorare described.

A. PHY Design

PHY in the IWS simulator is designed to model a wire-less modem operating around 5.8GHz in a low to middlespeed mobile outdoor environment. The transmission poweris 10dBm. The antennas used in both the transmitter andreceiver have a gain of 6dBi and the transmission loss isapproximately 3dB. Both antennas are 25 cm in length andcan be located on the rooftop of vehicles. Pilot symbolassisted QPSK modulation is used as well as a (63,51,5)BCH code for error detection and correction.

Proceedings of the 8th InternationalIEEE Conference on Intelligent Transportation SystemsVienna, Austria, September 13-16, 2005

WC5.2

0-7803-9215-9/05/$20.00 ©2005 IEEE. 461

0 50 100 150 200 250 300 350−100

−95

−90

−85

−80

−75

−70

−65

−60

−55

−50

Measured received power in an open area

Rec

eive

d P

ower

(dB

m)

Measured received power in an open areaComputed received power using two−ray model

Fig. 2. Received power computed by the two ray model and measureddata from an open area field test conducted in Columbus, Ohio.

B. Channel Models

PHY in the IWS simulator computes the received powerusing the following large scale path loss models:

1) Line of Sight - Two-Ray Model: A Line-of-Sight com-munication means that there is no obstruction between thetransmitting vehicle and the receiving vehicle. The receivedsignals are mainly contributed by a direct free space propaga-tion from the transmitter antenna and a strong reflected wavefrom the road surface. These two propagation paths constructthe two-ray channel model [4]. The received signal path lossLd(r) using this two-ray model is given in Eqn. (1) [5].Figure 2 shows the resulting received power computed byusing the two ray model and the open area field test resultconducted in Columbus, Ohio.

Ld(r) =(

λ

4π

)2∣∣∣∣∣e−j

2πrtλ

rt

+ Re−j

2πrrλ

rr

∣∣∣∣∣2

, (1)

where r is the distance between the transmitter and receiver;λ is the wavelength; rt is the direct distance from thetransmitter antenna tip to the receiver antenna tip; rr is thereflection path distance from the transmitter antenna tip tothe receiver antenna tip; and R is the complex reflectioncoefficient of the road-surface.

2) Building Blockage - Virtual Source Model: The pres-ence of buildings at the intersections might block Line-of-Sight communications when the transmitter and the receiverare travelling along perpendicular streets. However, the sig-nals are still possible to be received because of reflectionand diffraction. When Line-of-Sight is blocked by buildingsat the intersection, a virtual source is used to compute thepath loss at the receiver [6]. The virtual source acts as asecondary transmission source located in the center of theintersection. Its transmitted power is a fraction of the Line-of-Sight received power at that location and varies according tothe street characteristics. The path loss Ld,vs due to buildingblockage is given in Eqn. (2). It is guaranteed that thereis always Line-of-Sight between the virtual source and anyvehicles along all directions at the intersection.

Ld,vs = αLd(r1)Ld(r2), (2)

where r1 is the distance between the transmitter and thevirtual source; r2 is the distance between the receiver andthe virtual source; Ld(r1) and Ld(r2) are computed usingEqn. (1); and α is a street characteristic parameter.

3) Shadowing Effect: Large vehicles like trucks and busescause shadowing effect to smaller vehicles and act as ob-stacles to wireless communication. When the shadowinghappens, there is no Line-of-Sight communication. A knife-edge diffraction model is used in PHY for this scenario [5].The Fresnel-Kirchoff diffraction parameter ν is given by

ν = h

√2 (d1 + d2)λ (d1d2)

(3)

where h is the distance from the diffraction knife-edge tothe Line-of-Sight between transmitter and receiver; d1 isthe interval of Line-of-Sight between the transmitter and theknife-edge projection onto the Line-of-Sight; and d2 is theinterval of the Line-of-Sight between the receiver and theknife-edge projection onto the Line-of-Sight.

Approximated path loss in dB based on the Fresnel-Kirchoff diffraction parameter ν can be calculated by a setof regressions proposed by Lee [7] given in Eqn. (4)

Ld(dB) =

⎧⎪⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎪⎩

0 ν ≤ 020 log (0.5 − 0.62ν) −1 ≤ ν ≤ 020 log (0.5 exp (−0.95ν)) 0 ≤ ν ≤ 1

20 log(

0.4 −

√0.12 − (0.38 − 0.1ν)2

)1 ≤ ν ≤ 2.4

20 log(

0.225ν

)ν ≥ 2.4

(4)

C. Error Probability

When a strong Line-of-Sight is present, the Two-RayModel can accurately estimate the channel gain as shownin Figure 2. With proper channel estimation method, anAdditive White Gaussian Noise (AWGN) channel error per-formance can be achieved. For an AWGN channel, the biterror and word error probability can be calculated by usingthe received bit energy Eb and the ambient noise N0. Thebit error probability at the receiver is calculated using Eqn.(5).

Pe

(Eb

N0

)=

12erfc

(√Eb

N0

), (5)

where erfc() is the error function.When Line-of-Sight is not present, a Rayleigh fading

channel is used and the bit error probability at the receiveris computed using Eqn. (6) given Eb and N0.

Pe

(Eb

N0

)=

12

⎛⎝1 −

√√√√Eb

N0

1 + Eb

N0

⎞⎠ , (6)

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III. WIRELESS SIMULATOR (WS)

For wireless simulations, two distinct wireless simulatorsare used. The offline WS [2] provides a statistical repre-sentation of packet transmission behavior, focused on thepacket collision probability and delay. The simulator handlesa variety of configurable conditions, which include the MACprotocol, the building presence, the repeater presence, theinitial packet transmission distance, the packet transmissioninterval, the maximum number of retransmissions, and theretransmission interval.

VTS uses millisecond-level intervals to model the vehi-cle movements while wireless communication needs to besimulated at the microsecond level. Due to the differencein operation time scales, an online WS is introduced toperform the statistical approximation of wireless packettransmission behavior. The online simulator relies on thedata gathered from the offline wireless simulator [2] undera variety of simulation scenarios to estimate the appropriatepacket collision rates and packet transmission latencies asshown in Figure 1. It also accounts for the physical layercharacteristics to perform the actual determination of thesignal path loss and frame error rate.

A. Dolphin Protocol

The DOLPHIN [8] protocol is implemented in our wirelesssimulator. In DOLPHIN, time is divided into slots andeach vehicle is allowed to transmit one packet in each slotaccording to a non-persistent CSMA mechanism. When theMAC layer receives a new packet from the upper layer, itsstate changes from Idle to Broadcast. In this state, the nodechecks if it has sent any other packet in the current slot.If it has sent another packet before, the node waits for thenext slot. When the node finds a slot where it is allowed totransmit, it listens to the channel and sends its packet if thechannel is empty. On the other hand, if the node finds thechannel busy, state changes to Backoff and the node waitsfor a random amount of time before returning back to theBroadcast state. In our simulations, each slot is 20 ms andvehicles do 5 retransmissions for each packet.

B. Repeater

Broadcast communication has a challenge in urban areas.Especially in an urban area crowded with tall buildingsaround intersections, it is difficult to disseminate packets todifferent road segments shadowed by buildings. In this study,we are using repeaters located at the intersection which haveLOS to all road segments.

C. Message generator

The distance based message generator is part of IWS andis responsible for determining when vehicles will transmitmessages. The vehicles transmit on a distance based patternthat is determined by an initial transmission distance and atransmission interval. The message generator keeps track ofthe vehicle positions and when they move across a specifictransmission border, it triggers the message generation andthe vehicles attempt to transmit their position, velocity, and

acceleration up to 5 times. This distance based method isdifferent from the traditional time based method, in whichmessages are transmitted at constant time intervals. Thedistance based method is adopted to eliminate redundantmessage broadcasts in slow moving and stationary trafficconditions. Section VI will discuss the effects of differenttransmission intervals.

D. Computing the Successful Packet Delivery Probability

When the signal power is sufficient for a packet reception,the successful packet delivery probability determines whetherthe packet is successfully received or not. The packet colli-sion statistics gathered from the WS simulator for differentscenarios are used in the computation. (P r

c : packet collisionprobability at repeater, P v

c : packet collision probability at avehicle).

A packet has two possible paths to reach its destination,namely a direct path and an indirect path through therepeater, if present. The following successful packet deliveryprobabilities are defined for these paths.

Pdirect = (1 − P vc ).(1 − P vv

e ) (7)

Pindir = (1 − P rc ).(1 − P vr

e ).(1 − P vc ).(1 − P rv

e )(8)

where Pdirect is the probability that a direct packet trans-mission between the source and the receiver vehicle issuccessful; Pindir is the probability that an indirect packettransmission between the source and the receiver vehicleis successful; P vv

e is the frame error probability for atransmission between the source and the receiver vehicles;P vr

e is the frame error probability for a transmission betweenthe source vehicle and the repeater; and P rv

e is the frameerror probability for a transmission between the repeaterand the receiver vehicle. The frame error probabilities areprovided by the physical layer.

Using Pdirect and Pindir , the total successful packetdelivery probability (Psuccess) can be computed as:

Psuccess = 1 − (1 − Pdirect).(1 − Pindir) (9)

IV. ITS COMPONENTS

A. Vehicle Traffic Simulator

Vehicle traffic simulator (VTS) is responsible for provid-ing realistic, microscopic-level traffic flow information tothe wireless simulator. The simulator is developed underVC++ environment. A graphic user interface (GUI) is alsodeveloped to facilitate simulation and visualize the trafficflow. In doing so, it has to account for such elements asintersection layout, vehicle throughput, vehicle types, androute selection. It is composed of road management, vehiclemanagement and signal management components [1], [9].As shown in Figure 3, the graphical user interface (GUI) ofthe IWS simulator represents an urban four-way intersection.After the user selects the simulated scenario, the GUI canoffer the real time simulation of the traffic flow and warningmessages in this urban intersection.

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Fig. 3. Intersection Warning System Simulator Graphical User Interface

B. Collision Warning System

The IWS is capable of simulating different collision warn-ing systems. The collision warning system computes theprobability of route contention for a given vehicle basedon received messages and expected destinations of vehicles.A collision is detected in the simulator when two vehicleshas overlapped body. Based on the immediacy of a colli-sion possibility, it will issue warning messages to drivers.Collision detection is a complex task given that there isa large set of variables involved, including vehicle speed,acceleration/deceleration rate, and direction. In this study, athree-level collision warning system algorithm is chosen. Itswarning levels are defined as level 1 (elevated), level 2 (high)and level 3 (severe).

C. Driver’s Behavior Model

Modelling driver behavior is a complex but necessaryresponsibility within VTS since not all drivers behave andreact the same way. This module defines a set of driver typesand distributes each type among the vehicles. The drivermodel determines the driver’s acceleration, deceleration, andresponse to warnings. For this purpose, a classification ofdrivers’ characteristics is needed. Without loss of generality,the drivers are grouped into three categories (aggressive, nor-mal and conservative) based on their desired speed [10], [11].Though driver response time is independent of driver’scharacteristics, driver response motion is assumed to dependon drivers’ characteristics. Drivers may release initial accel-erator only, decelerate, or brake, depending on their own

Level 1 Level 2 Level 3Aggressive N/A Initial Accelerator Decelerate

ReleaseNormal Initial Accelerator Decelerate Brake

ReleaseConservative Decelerate Brake Brake

TABLE I

DRIVER RESPONSE MOTION MODEL

characteristics and the warning level [12], [13], [14]. Theresponse motion is summarized in Table I.

V. SIMULATION ENVIRONMENT

The urban intersection layout is depicted in Figure 3. It isa four-way crossing, where traffic can travel in any directionon the appropriate lanes. Depending on the specific scenariobeing analyzed, a traffic signal is present in the middle ofthe intersection to direct all incoming traffic. Some importantparameters are discussed below.

Vehicle types: There are four vehicle types in our simu-lation: passenger vehicles (cars), buses, trucks, and motor-cycles. A vehicle’s type determines not only its physicaldimensions and properties, but also dictates its behavior intraffic and has significant impact on wireless transmissionbehavior. This is because each vehicle type has differentantenna heights and shadowing effect.

Speed limit: This is the maximum speed at which ve-hicles travel. Given that the driver behavior and intersectionconditions ultimately dictate vehicle speeds, this value servesas a general guideline for average vehicle speed. Based on

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the message generation mechanism described in Section III-C, actual vehicle speed affects the frequency at whichinformation is transmitted. The speed limit is set to 45 mph(20 m/s).

Initial transmission distance: It determines the furthestpoint where vehicles start broadcasting their positions. Largerdistances maximize the opportunity of advanced notificationsfor collision warnings; however, it increases the packet loadin the wireless network. The initial transmission distance isset to 100 m.

Transmission interval: The transmission intervals are thedistances between transmission borders.

Intersection vehicle throughput: Vehicle throughput isthe target average number of vehicles that enter and leavethe intersection area once the simulation reaches a steadystate. Even though traffic patterns are intentionally non-deterministic, a concerted effort is made to maintain theoverall throughput at the specified level. Determining theappropriate set of vehicle throughput values is essential toevaluate performance across a representative range of trafficconditions. Based on the level of service (LOS) [15] ofthe intersection, the simulated vehicle throughput values are1200, 1800, 3600, 5200, 7200, and 12000 vehicles/hour.

Traffic signal presence: The traffic signal controls theflow of the vehicle traffic within the intersection. Its presenceis optional.

Repeater presence: The repeater has a line of sight to allroad segments of an intersection. Its presence is optional.

Number of vehicles: This value determines the totalnumber of vehicles that will pass through the intersectionduring the simulation period. Effectively, this parameterdictates the length (in time) of the simulation.

VI. SIMULATION RESULTS

A. Wireless Simulator

The offline WS is run with 288 different scenarios tocollect average packet collision rate and delay statistics. Inthese simulations, we have examined the effect of build-ing presence, repeater presence, vehicle density and packettransmission interval to the packet collision rate and delaystatistics. The strongest relationship that surfaces from resultsis between the packet transmission intervals and the packetcollision rates.

Due to space limitation, we will present only one ofthe scenarios which includes buildings, the repeater and thetraffic signal. The average packet collision rate for differentpacket transmission intervals and vehicle throughput valuescan be seen in Figure 4. In the figure, different curves belongto different intervals between transmission borders. Whenthe intervals between the transmission borders are shorter,the collision rate is higher. This is because, shorter datatransmission intervals translate to an increased utilization ofthe wireless medium due to a larger number of transmissions.This increase in the total number of transmissions leads toan increase in the number of packet collisions. Similar tothe shorter transmission intervals, higher vehicle throughput

Fig. 4. Packet Collision Rate

also increases the total number of packet transmissions andpacket collisions.

In this paper, transmission latency refers to the timeelapsed between the instant the packet enters the sourcequeue and the reception time of the packet by anothervehicle. In the worst case delay scenario where the vehiclethroughput is in the saturation region for the signalizedintersection and the transmission interval is shortest (5 m),the average delay is 23 ms. The delay distribution for thisscenario is shown in Figure 5. Note that all of the successfulpackets have a delay value below 120 ms.

0 20 40 60 80 100 1200

5

10

15

20

25

30

35

40

45

50

Freq

uenc

y (%

)

Transmission latency (msec)

Traffic Signal; All buildings; 7200 (vehicles/hour); Repeater

Fig. 5. Packet Delay Distribution

B. Percentage of Vehicle Collisions

One important metric used to evaluate the performanceof IWS is the vehicle collision percentage (CP ). In oursimulations, the worst case condition is considered wheredrivers keep their desired velocity unless they receive awarning message. In order to evaluate the system froma macro point of view, the number of collisions duringthe simulation is counted and the collision percentage iscomputed as:

CP =collided vehicles

total number of vehicles(10)

465

1000 2000 3000 4000 5000 6000 7000 8000

10%

15%

20%

25%

30%

35%

40% No IWS IWS (50 m) IWS (20 m) IWS (10 m) IWS (5 m)

Col

lisio

ns P

erce

ntag

e

Traffic Flow (Vehicles/hour)

Fig. 6. Percentage of vehicle collisions at signalized, all buildings, repeaterintersection

Fig. 7. Collision Speed and Collisions Percentage (1800 Vehicles/hour)

Figure 6 shows that IWS significantly reduces the vehiclecollision percentage. In addition, shortening the transmissioninterval further lowers the collision percentage. As can beseen in Figure 6, at low flow rates, the collision percentagegrows with increasing the flow rate. On the other hand, analmost constant collision percentage is observed at higherflow rates. This is because of the congestion build up atthe intersection. When the intersection reaches its saturationpoint, further increasing the target rate does not increase thecollision percentage.

C. Collision Speed

The performance of the IWS can also be evaluated bycollision speed when the collision is unavoidable. Since theseverity of collisions depends on the collision speed, a lowercollision speed is desirable.

Figure 7 depicts the average collision speed for flow rate of1800 vehicles/hour. The collision percentages are also plottedon the same figure. Figure 7 clearly shows that, IWS and

short transmission interval reduces the collision speed. Sametrend is observed for the collision percentage.

VII. CONCLUSION

In this paper, the Intersection Warning System simula-tor is presented with detailed network and physical layermodelling. For the MAC layer abstraction, a large set ofsimulations is done. Statistical results from these simulationsare used in the IWS simulator with the physical layer modelto decide the set of vehicles which receive the warningssuccessfully. In our system, a distance based packet generatoris implemented. According to this approach, vehicles broad-cast their positions, speeds, and directions when they passpredetermined transmission borders. Although shortening thedistance between these borders increases the packet collisionprobability, simulation results show that shorter intervalscan more effectively avoid vehicle collisions and lower thecollision speed.

REFERENCES

[1] A. Dogan, G. Korkmaz, Y. Liu, F. Ozguner, U. Ozguner, K. Redmill,O. Takeshita, and K. Tokuda, “Evaluation of intersection collisionwarning system using inter-vehicle communication simulator,” inProceedings of Intelligent Transportation Systems, 2004, pp. 1103–1108.

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[10] J. Lei, K. Redmill, and U. Ozguner, “VATSIM: A Simulator for Ve-hicles and Traffic,” in Proceedings of IEEE Intelligent TransportationSystems Conference, Oakland, CA, August 2001, pp. 686–691.

[11] K. Redmill and U. Ozguner, “VATSIM: A Vehicle and Traffic Sim-ulator,” in Proceedings of IEEE Intelligent Transportation SystemsConference, 1999, pp. 656–661.

[12] B. Dariush and K. Fujimura, “A framework for driver specific in-ference of danger at signalized intersections,” in Proceedings of In-telligent Transportation Systems, 1999 IEEE/IEEJ/JSAI InternationalConference, 5-8 Oct. 1999, pp. 195 – 200.

[13] R. Miller and Q. Huang, “An adaptive peer-to-peer collision warningsystem,” in Proceedings of Vehicular Technology Conference, VTCSpring 2002, vol. 1, May, pp. 317 – 321.

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