Research Article Usability Analysis of Collision Avoidance System …downloads.hindawi.com/journals/jam/2014/951214.pdf · based on a collision detection system, AEB logic, and the

Research ArticleUsability Analysis of Collision Avoidance System inVehicle-to-Vehicle Communication Environment

Hong Cho Gyoung-Eun Kim and Byeong-Woo Kim

Department of Electrical Engineering University of Ulsan No 93 Daehak-ro Nam-gu Ulsan Republic of Korea

Correspondence should be addressed to Byeong-Woo Kim bywokimulsanackr

Received 31 January 2014 Accepted 4 March 2014 Published 10 April 2014

Academic Editor Young-Sik Jeong

Copyright copy 2014 Hong Cho et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Conventional intelligent vehicles have performance limitations owing to the short road and obstacle detection range of the installedsensors In this study to overcome this limitation we tested the usability of a new conceptual autonomous emergency braking(AEB) system that employs vehicle-to-vehicle (V2V) communication technology in the existing AEB system To this end a radarsensor and a driving and communication environment constituting the AEB systemwere simulated the simulation was then linkedby applying vehicle dynamics and control logic The simulation results show that the collision avoidance relaxation rate of V2Vcommunication-based AEB system was reduced compared with that of existing vehicle-mounted-sensor-based system Thus amethod that can lower the collision risk of the existing AEB system which uses only a sensor cluster installed on the vehicle isrealized

1 Introduction

In recent years with the strict enforcement of safety regula-tions for vehicles consumer interest in vehicle safety is grow-ing Therefore studies on active safety systems and advanceddriver assistance systems (ADASs) have been actively takenup with the aim of ensuring safety including vehicle controlfor accident avoidance and mitigation these features are incontrast with those of conventional passive systems whichensure safety through simple warnings [1] One such promi-nent active system is the autonomous emergency braking(AEB) system In a recent survey the European Union (EU)determined that introducing the AEB system could reducethe annual number of deaths and serious injuries in vehicleaccidents by more than 8000 and 20000 respectively [2]Generally an AEB system employs environment-recognitionsensors such as radar lidar and cameras for detecting riskfactors [3 4] However the existing sensor-based systemsare able to detect only those vehicles that are within theemployed sensorsrsquo measurement ranges and blind spots mayoccur owing to obstacles In addition under bad weatherconditions detection becomes impossible or the detectionaccuracy drops significantly For overcoming the limitations

of sensor-based systems recently with the advancement of ITtechnology cooperative safety system has been introducedThis system is grafted with vehicle safety communicationschemes such as vehicle-to-vehicle (V2V) communicationand vehicle-to-infra (V2I) communication [5]

Currently international standards for AEB systems arebeing formulated worldwide and various studies on AEBsystems are being conducted The existing studies on AEBsystems were conducted based on the performance of sensorsemployed in vehicles and therefore they have limitationsconcerning detection area [6] For overcoming these lim-itations the current study was conducted based on thecooperative safety system grafted with V2I communication[7] A limitation of the AEB system that is the blind zoneoccurring at a crossroad was partially solved through V2Icommunication by employing radars in traffic lights at cross-roads However compared with direct V2V communicationV2I communication suffers from real-time limitations andlimited detection areas of the sensors installed on the roadsurface In the case of V2V communication studies on theoperating environment and evaluations of pure communica-tion technologies were conducted using the existing sensor-basedADAS and basic studies on cooperative adaptive cruise

Hindawi Publishing CorporationJournal of Applied MathematicsVolume 2014 Article ID 951214 10 pageshttpdxdoiorg1011552014951214

2 Journal of Applied Mathematics

Vehicle-mounted

- Radar- Camera

Common parts Infra

- Driving and communicationenvironmentNeighborhood vehicle

- Vehicle state

V2VWireless communication modulePositioning system

Collision detection system

AEB logic

Configuration scenario

Vehicle trajectoryprediction

(V2V)

Ego vehicle

dynamics

Collision warning index

Level 3

Level 2

Level 1

lowast

lowast

lowast

lowast

lowast

Sensor based on TTCminus1

Figure 1 Block diagram of analysis model

Normal Jerk brake Partial braking Full brake

TTC (s)

TTC gt 20 sBraking force 00 g

TTC le 20 sBraking force minus03 g



Figure 2 AEB logic

Camerasensor

Radarsensor

Lane information

Relative distance

TTC-based collision detection

Ego vehicleDisplay

warning vehicle

Collisionavoidance

system(AEB system)

Relative speed

Relative distance

Figure 3 Block diagram of vehicle-mounted-sensor-based AEBsystem

control were conducted by grafting ADAS with adaptivecruise control and realizing intervehicle communication [8ndash11] Consequently a presentation on the usability of the newconceptual AEB system that employs V2V communicationwas required

Therefore in this study to overcome the aforemen-tioned limitations of vehicle-mounted-sensor-based systemswe propose a new conceptual AEB system that employsV2V communication along with environment recognition

sensors In addition the usability of V2V communicationwascomparedwith that of vehicle-mounted sensors bymodifyingan existing vehicle-mounted-sensor-based AEB system toincorporate V2V communication

2 AEB System Design

21 AEB System Analysis Model As shown in Figure 1 foranalyzing the usability ofV2Vcommunication in comparisonwith that of vehicle-mounted sensors a detailed modelincluding various sensors and modules the driving andcommunication environment and vehicle dynamic charac-teristics was required Therefore in this study PreScan acommercial simulation code was used for modeling wirelesscommunication modules high-precision location measure-ment systems the driving and communication environmentand the radar and camera sensors installed in a vehicle Inaddition to determine the dynamic characteristics of a uservehicle equipped with the AEB system a full car modelwith multiple degrees of freedom was generated in CarSima vehicle dynamic behavior simulation software applicationand used Finally after interfacing PreScan and CarSimthrough MATLABSimulink an analysis model was builtbased on a collision detection system AEB logic and thesetting of a given scenario

Journal of Applied Mathematics 3

Camerasensor

Positioningsystem

Wirelesscommunication module

Kalman filter

In-vehiclesensor

Lane information

Position heading

VelocityPositionheading

and velocity

Collision detectionsystem

Target Vehicle 1 Target Vehicle 2 Target Vehicle 3

Ego vehicle

Displaywarning vehicle

Estimated position

Headingvelocity and

Collisionavoidance

system(AEB system)

middot middot middot


Figure 4 Block diagram of V2V communication-based AEB system

Direction of ego vehicle

Quadrant 1

quadrant 2quadrant 3

Quadrant 4 forward left

Forward rightRear right

rear left Vehicle 1

Ego vehicle

Vehicle 2

Relative distance1

+180∘ relative angleminus180∘ relative angle

(xego yego) 1205791

Relative 1205791

(x1 y1)

x1

x2

minus90∘ relative angle

90∘ relative angle

0∘ relative angle

1205792

(x2 y2)

egorsquos lateral

egorsquos longitudinalx-axis of

y-axis of

Figure 5 Principle of collision detection system

22 AEB System AEB is an active safety system thatmeasuresthe degree of risk between a user vehicle and a forward vehicleusing vehicle-installed environment recognition sensors suchas radars or cameras It helps in preventing accidents throughautomatic brake control in risky situations In Europe theenforcement of rules concerning AEB systems from 2014 hasbeen initiated In Europe the rules concerning AEB systemshave come into effect since the beginning of 2014 In 2009 itwas proposed that an informal group called the autonomousemergency braking system (AEBS)lane departure warningsystem (LDWS) informal group will be formed under theWorking Party on Brakes and Running Gear (GRRF) a sub-sidiary body of aWorld Forum for Harmonization of Vehicle(WP29) in order to formulate AEBSLDWS standards Eco-nomicCommission for Europe (ECE) regulations concerningAEBS are being enacted under the United Nations EconomicCommission for Europe (UNECE) [12]

Table 1 AEB system braking force

TTC (s) Braking force (g)le20 03le16 06le07 10

The AEB system described in this paper meets therequired performance specifications defined by the AEBGroup The time of application of automatic braking forceby the AEB system was determined based on a collision riskindex that is time-to-collision (TTC) of the user vehicle anda forward vehicle TTC can be calculated using (1) whichis based on the relative speed and relative distance betweenthe user vehicle and forward vehicle Table 1 and Figure 2 listand show respectively the braking force of the AEB system


-axis ofegorsquos lateral

-axis of

egorsquos lateral

-axis ofegorsquos longitudinal

-axis of

egorsquos longitudinal

E90∘

N0∘

W270∘

S180∘

(x998400 y998400 )

120593998400

x

x

x

x

998400y

y

y

y

998400

120593 = 0∘

120593 = 0∘


120593 =120593998400∘

120593=120593 998400∘

Figure 6 CSego transformation with azimuth change


Forward left warning zone number 2

Rear warning zone number 4

Rear left warning zone number 3

Rear right warning zone number 5 Forward right warning zone number 6

Egovehicle

TargetvehicleForward warning zone number 1

EV ego vehicleTV target vehicle

120579f forward relative angle120579r rear relative angle

+120579r∘

minus minus120579r∘

+120579f∘

120579f∘

(90∘ sim (180∘ minus 120579r∘)) (120579f

∘ sim 0∘)

(minus120579f∘ sim minus90∘)(minus90∘ sim (minus180∘ + 120579r

∘))

Figure 7 Warning zone corresponding to relative angle

and AEB logic according to changes in TTC here 119892 denotesacceleration due to gravity and it is taken as 98ms2 [7]Consider

TTC (s) = Relative distanceRelative speed

(1)

23 Vehicle-Mounted-Sensor-Based AEB System Generallya vehicle-mounted-sensor-based AEB system comprises acamera sensor and long- and short-distance radar sensorsSensor specifications were determined by referring to thespecifications of an actual commercial product Table 2 liststhe specifications of each sensorThe camera sensormountedin the front provides information about the traffic in a laneand the relative distance to a forward obstacle Long- andshort-distance radar sensors can be used for measuring thedistance and speed relative to an obstacle within the forwarddetection area In this study to compensate for the limitationsof the camera and radar sensors the distance and speedrelative to a forward obstacle were measured through sensorfusion [13] The TTC was calculated using the measuredinformation and used as the brake input reference for

the AEB system Figure 3 shows a block diagram of a vehicle-mounted-sensor-based AEB system

24 V2V Communication-Based AEB System The V2Vcommunication-based AEB system described in this paperwas operated based on the collision detection system pro-posed herein During operation the system received infor-mation about the nearby vehicles and the user vehiclethrough V2V communication and employed it for operationFigure 4 shows the overall system configuration

First the location measuring system provides the vehiclelocation and heading angle information this system includeda noise model corresponding to the tolerance specification(Table 3) of commercial differential global positioning sys-tems (GPS)The received GPS coordinates of the user vehicleand nearby vehicles were expressed in the 119909 119910 coordinatesystem The 119909 119910 coordinate system defines a tangent planein the GPS coordinates of the Infrasystem within 1 km the119909-axis was defined toward the east and the 119910-axis towardthe north [14] Next various sensors employed in the uservehicle transmit various types of information such as speedacceleration and yaw rate through the vehiclersquos internal


Table 2 Vehicle-mounted sensorsrsquo specifications

Parameter

Camera sensorFocal length (mm) 6

FoV (∘) Azimuth plusmn205Elevation plusmn135

Resolution (pixels) 752 times 480

Short-range radar Detection range (m) 02ndash30


Long-range radar Detection range (m) 2ndash200


Table 3 Module and sensor specifications of V2V communication-based AEB system

Parameter




Position system (DGPS) Accuracy (cm) 50

Wireless communication module (V2V) Communication range (m) 1000

Message frame Basic safetyMessage (BSM)

Positioning system and in-vehicle sensors

V2V wirelesscommunication module

Data fusion

Ego and target vehicletrajectory prediction


Collision prediction

Start

Yes

Collision warning

Collision avoidance control(AEB system)

End

No

Figure 8 Flowchart of V2V communication-based AEB system

communication system Finally the wireless communicationmodule employed for intervehicle communication providesinformation about the nearby vehicles a message framereceived through the intervehicle communication channelwas applied in conjunction with the basic safety message

Heavy fogDriverrsquos view

1 23

vehicle driving direction

Ego20m 75m

visibility 50m

Figure 9 Initial road condition

(BSM) standards defined in SAE J2735 [15] In addition errorof the location measurement systems in the user vehicle andnearby vehicles was calibrated by employing a Kalman Filtertrajectories of the userrsquos and nearby vehicles were measured[16 17]

As shown in Figure 5 the V2V communication-basedcollision detection system proposed in this study determinesthe location of and distance to a nearby vehicle after gener-ating CSego a Cartesian coordinate system with the currentlocation (xego yego) of the user vehicle as its origin CSegoexpresses the longitudinal direction along the 119909-axis and thetransverse direction along the 119910-axis with reference to theuser vehiclersquos driving direction The locations of nearby vehi-cles in CSego are divided along the quadrants of CSego andexpressed in relative coordinates (119909119899 119910119899) (119899 = vehicle id)


(a) Simulation viewer

0 2 4 6 8 10 12 14 16 1800

02

04

06

08

10

Col

lisio

n de

tect

ion

(b) Collision detection flag

Time (s)

0 2 4 6 8 10 12 14 16 18

0

2

4

6

8

10

12

14

16

18

20

TTCx

Time (s)

(c) TTCx

0 2 4 6 8 10 12 14 16 18

00A

x (g

)

Time (s)

minus10

minus08

minus06

minus04

minus02

(d) AEB system command

0 2 4 6 8 10 12 14 16 180

50

100

150

200

250

300

350

400

Relat

ive d

istan

ce (m

)

Time (s)

(e) Relative distance

0 2 4 6 8 10 12 14 16 180

2

4

6

8

10

12

14

16

18

20

Relat

ive s

peed

(ms

)

Time (s)

(f) Relative speed

Figure 10 Vehicle-mounted-sensor-based AEB system


after comparing the current location information of the uservehicle and nearby vehicles received through intervehiclecommunication The relative angle 120579n was calculated bycomparison with the azimuth 120593 which represents the uservehiclersquos driving direction As shown in Figure 5 the nearbyvehicle observation system can recognize the locations ofnearby vehicles based on the relative angle 120579119899 which variesalong the quadrant As shown in Figure 6 120593 varied with theuser vehiclersquos heading angle and was set to be 0∘ with respectto the east direction

As shown in Figure 6 the coordinate axis of CSegorotated according to changes in 120593 The longitudinal drivingdirection of the user vehicle was always matched with the 119909-axis using the rotational transformation matrix equation (2)which considers the coordinate axisrsquo rotation

[1199091015840

1199101015840] = [

cos120593 sin120593minus sin120593 cos120593] [

119909

119910] (2)

As shown in Figure 7 the warning zone was determinedbased on the relative angle obtained following the aboveprocess 120579119891 and 120579119903 are the error ranges of the relative anglein the case that the user vehicle and all vehicles in the sametraffic lane move in the middle of the traffic lane

The relative angle relative distance and heading angleof the nearby vehicle are the parameters that determine thewarning zone The heading angle is an important parameterfor determining the nearby vehiclersquos driving direction

The relative heading angle between the user vehicle andthe nearby vehicle was obtained for determining whetherthe nearby vehicle is driving in the same direction as theuser vehicle entering a crossroad or driving in the oppositetraffic lane In addition the relative distance can be obtainedusing the user vehiclersquos barycentric coordinate system CSegoHowever the relative distance obtained thus does not accountfor the size of the vehicle therefore the relative distancewas calibrated assuming a circular vehicle [18] The TTCwas calculated based on the obtained relative distance andrelative speed of the user vehicle and the nearby vehicleand the calculated TTC was used as the collision risk indexfor the AEB system Figure 8 shows a flowchart of the V2Vcommunication-based AEB system

3 Simulation and Results

31 Simulation Scenario As shown in Figure 9 the driv-ing direction and scenarios were defined for a compara-tive analysis of the vehicle-mounted-sensor-based and V2Vcommunication-based AEB systems The driving directionwas determined based on the conditions under which acci-dents generally occur This includes the condition of heavyfog under which visibility is less than 50m which makes itdifficult for a driver to recognize forward risk situations

An ego vehicle mounted with an AEB system can avoidand mitigate the effects of collisions in the longitudinaldirection Consider the following scenario The driver ofVehicle 1 changes lanes to avoid collision after finding thatVehicle 2 in front is stationary Vehicle 3 is driving in theopposite traffic lane The simulation scenario is summarizedin Table 4

Blind zone

Figure 11 Limitations of vehicle-mounted sensor

Table 4 Simulation scenario

Vehicle Initial speed End speed NoteEgo 50 kmh 60 kmh AEB systemVehicle 1 60 kmh 60 kmh Lane changeVehicle 2 55 kmh 0 kmh Vehicle faultVehicle 3 70 kmh 70 kmh Opposite lane

32 Simulation Results Simulation was performed byemploying the vehicle-mounted-sensor-based AEB systemand the V2V communication-based AEB system in thescenarios defined in Table 4

Figure 10 shows the simulation results of the vehicle-mounted-sensor-based AEB system The system was capableof detecting forward vehicles only such as the ego in thiscase located in the traffic lane Therefore the longitudinalcollision risk index TTCx shown in Figure 10(c) increasedgradually from 0 s to about 11 s and then decreased rapidlyThis is because Vehicle 1 which was running initially on thesame traffic lane changed lanes owing to the detection ofa stationary vehicle the sensor in the Ego vehicle detecteda stationary vehicle (Vehicle 2) on the same traffic lane Asshown in Figure 10(d) it can be seen that the AEB systemwas applied normally with braking force as TTCx variedHowever it can be confirmed from the relative distance graphin Figure 10(e) that collision was predicted when the relativedistance changed to 0m In fact even in the simulationenvironment the occurrence of a collision can be confirmedbased on the vehicle driving state shown in Figure 10(a) andthe collision detection flag shown in Figure 10(b) In additiona comparison of the relative speed before the time (about148 s) of braking force application by the AEB system andthe relative speed at the time of collision shown in therelative speed graph of Figure 10(f) indicates that the speedwas reduced by about 18 kmh (05ms) Therefore it can besaid that the collision avoidance relaxation rate achieved withthe vehicle-mounted-sensor-based AEB system in relevantscenarios was not more than 3 This can be ascribed tothe vehicle-mounted sensorsrsquo inability to detect the stationaryvehicle (Vehicle 2) on the road ahead owing to the presenceof a blind zone due to a front vehicle as shown in Figure 11

In contrast in the case of the V2V communication-basedAEB system shown in Figures 12(a) and 12(b) there was nocollision between the Ego vehicle and the stationary vehicle(Vehicle 2) The TTCx results shown in Figure 12(c) indicate


00 02 04 06 08 1000

02

04

06

08

10

Time (s)(a) Simulation viewer

0 2 4 6 8 10 12 14 16 180

2

4

6

8

10

12

14

16

18

20

TTC

x

Time (s)

Vehicle 2 Vehicle 1

(c) TTCx

0 2 4 6 8 10 12 14 16 18

00

Ax

(g)

Time (s)

minus10

minus08

minus06

minus04

minus02


0 2 4 6 8 10 12 14 16 180

50

100

150

200

250

300

350

400

Rela

tive

dist

ance

(m)

Time (s) Vehicle 1 Vehicle 2

Vehicle 3


0 2 4 6 8 10 12 14 16 18

0

2

4

6

8

10

12

14

16

18

20

Rela

tive

spee

d (m

s)

Time (s)

Vehicle 1 Vehicle 2

Vehicle 3

(f) Relative speed

Col

lisio

n de

tect

ion


Figure 12 V2V communication-based AEB system


0 2 4 6 8 10 12 14 16 18

2

3

4

5

6

7

War

ning

zone

Time (s)

1

Vehicle 1 Vehicle 2

Vehicle 3

Figure 13 Warning zone change during simulation

that there was a collision risk in the longitudinal directionwith Vehicles 1 and 2 which were located in front of theEgo vehicle However Vehicle 3 was not represented in theTTCx graph Vehicle 3 was determined to be a vehicle inthe opposite traffic lane based on heading angle informationreceived through V2V communication and was excludedby the collision detection system It can be inferred fromFigure 12(d) that braking force can be applied stably basedon changes in the TTCx calculated by the collision detectionsystem after predetecting the forward stationary vehicle(Vehicle 2) through intervehicle communication In additionthe plots of relative distance (Figure 12(e)) and relative speed(Figure 12(f)) indicate that the collision avoidance relaxationrate reached 100 with the avoidance of collision because therelative speed decreased to 0ms before the relative distanceto Vehicle 2 decreased to 0m In addition it can be seen fromFigure 12(f) that the relative speed with respect to Vehicle 2increased slowly after about 9 s indicating that the vehiclebecame stationary at about 9 s

Figure 13 shows thewarning zone according to simulationtime It can be seen that Vehicle1 which was running in frontof the Ego vehicle changed its traffic lane after detecting astationary vehicle Thus the warning zone of Vehicle 1 waschanged from 1 to 6 Vehicle 2 was the stationary vehicle andthere was no change in its traffic lane thus there was nochange in its warning zone Finally Vehicle 3 was drivingon the opposite traffic lane and its warning zone changedfrom 2 to 3 at about 115 s because Vehicle 3 overtook theuser vehicle This can also be seen in the relative distancegraph (Figure 12(e)) which shows that the relative distancewith respect to Vehicle 3 was closer to 0 at about 115 s andstarted increasing thereafter

4 Conclusions

In this study the usability of the proposed V2Vcommunication-based AEB system was compared withthat of the existing vehicle-mounted-sensor-based system

An analysis model was built for determining the usability ofthe V2V communication-based AEB system The analysismodel considered the vehicle-mounted sensor and V2Vcommunication environments Furthermore the existingvehicle-mounted-sensor-based AEB system was realizedusing this model In addition a new conceptual AEBsystem was proposed and developed by combining V2Vcommunication technology with environment-recognitionsensors Then a comparative analysis simulation of theV2V communication-based AEB system versus the vehicle-mounted-sensor-based system was conducted in the samescenario The simulation results show that in the case ofthe existing vehicle-mounted-sensor-based AEB systembraking application time lengthened and a collision occurredowing to the systemrsquos detection area limitation Howeverin the case of the V2V communication-based AEB systemcollision was avoided regardless of driving conditions andobstacles through collision risk detection within the rangeof intervehicle communication In addition in the case ofthe existing vehicle-mounted-sensor-based AEB system thecollision avoidance relaxation rate was no more than 3 Incontrast in the case of the V2V communication-based AEBsystem the collision avoidance relaxation rate reached 100

Therefore the usability of the V2V communication tech-nology was demonstrated through the aforementioned com-parative analysis Future studies will be aimed at testing theproposed system in the V2V communication environmentwith an actual vehicle used in practice and analyzing the pro-posed system in various scenarios and driving environments

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was supported by the Ministry of ScienceICT and Future Planning (MSIP) Korea under the Con-vergence InformationTechnologyResearchCenter (C-ITRC)support program (NIPA-2013-H0401-13-1008) supervised bythe National IT Industry Promotion Agency (NIPA) Andthis paper is an extended and improved version of the paperaccepted for the KCIC-2013FCC-2014 Conferences

References

[1] M Lu K Wevers R Van Der Heijden and T Heijer ldquoADASapplications for improving traffic safetyrdquo in Proceedings of theIEEE International Conference on Systems Man and Cybernetics(SMC rsquo04) vol 4 pp 3995ndash4002 October 2004

[2] Economic Commision for Europe ldquoAn introduction to the newvehicle safety regulationrdquo Tech Rep WP92-145-08 InformalDocument Geneva Switzerland 2008

[3] K Goswami G S Hong and B G Kim ldquoA novel mesh-basedmoving object detection technique in video sequencerdquo Journalof Convergence vol 4 no 3 pp 20ndash24 2013


[4] Verma V Jain and R Gumber ldquoSimple fuzzy rule-based edgedetectionrdquo Journal of Information Processing Systems vol 9 no4 pp 575ndash591 2013

[5] D Caveney and W B Dunbar ldquoCooperative driving beyondV2V as an ADAS sensorrdquo in Proceedings of the IEEE IntelligentVehicles Symposium (IV rsquo12) pp 529ndash534 June 2012

[6] N Kaempchen B Schiele and K Dietmayer ldquoSituation assess-ment of an autonomous emergency brake for arbitrary vehicle-to-vehicle collision scenariosrdquo IEEE Transactions on IntelligentTransportation Systems vol 10 no 4 pp 678ndash687 2009

[7] J Lee S Jo J Kwon T Hong and K Park ldquoDevelopmentof V2I-based Intersection Collision Avoidance Systemrdquo inProceedings of the Conference for Korea Institute of ITS pp 90ndash96 May 2013

[8] G Peng K Zeng and X Yang ldquoA hybrid computational intelli-gence approach for the VRP problemrdquo Journal of Convergencevol 4 no 2 pp 1ndash4 2013

[9] S Wang A Huang and T Zhang ldquoPerformance evaluationof IEEE 802154 for V2V communication in VANETrdquo inProceedings of the International Conference on Computationaland Information Sciences pp 1603ndash1606 June 2013

[10] Z Taysi and A Yavuz ldquoETSI compliant GeoNetworking proto-col layer implementation for IVC simulationsrdquoHuman-CentricComputing and Information Sciences vol 3 no 1 p 4 2013

[11] C Desjardins and B Chaib-Draa ldquoCooperative adaptive cruisecontrol a reinforcement learning approachrdquo IEEE Transactionson Intelligent Transportation Systems vol 12 no 4 pp 1248ndash1260 2011

[12] Economic Commission for Europe Automatic EmergencyBraking and Lane Departure Warning Systems UNECETRANSWP29GRRF-65-Inf19 Informal Group on AutomaticEmergency Braking and Lane Departure Warning SystemGeneva Switzerland 2009

[13] J S Sangorrin J Sparbert U Ahlrichs W Branz and OSchwindt ldquoSensor data fusion for active Safety Systemsrdquo SAEInternational Journal of Passenger CarsmdashElectronic and Electri-cal Systems vol 3 no 2 pp 154ndash161 2010

[14] ldquoPreScan R6 6 0 Help Manualrdquo May 2013[15] C Hedges and F Perry ldquoOverview and use of SAE J2735

message sets for commercial vehiclesrdquo SAE Technical Paper2008-01-2650 October 2008

[16] J Huang and H-S Tan ldquoA low-order DGPS-based vehiclepositioning system under urban environmentrdquo IEEEASMETransactions on Mechatronics vol 11 no 5 pp 567ndash575 2006

[17] S Ammoun F Nashashibi and C Laurgeau ldquoReal-time crashavoidance system on crossroads based on 80211 devices andGPS receiversrdquo in Proceedings of the IEEE Intelligent Transporta-tion Systems Conference (ITSC rsquo06) pp 1023ndash1028 September2006

[18] YWang ldquoVehicle collision warning system and collision detec-tion algorithm based on vehicle infrastructure integrationrdquo inProceedings of the 7th Advanced Forum on Transportation ofChina (AFTC rsquo11) pp 216ndash220 October 2011

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Stochastic AnalysisInternational Journal of


Vehicle-mounted

- Radar- Camera

Common parts Infra

- Driving and communicationenvironmentNeighborhood vehicle

- Vehicle state

V2VWireless communication modulePositioning system


AEB logic

Configuration scenario

Vehicle trajectoryprediction

(V2V)

Ego vehicle

dynamics

Collision warning index

Level 3

Level 2

Level 1

lowast

lowast

lowast

lowast

lowast

Sensor based on TTCminus1

Figure 1 Block diagram of analysis model

Normal Jerk brake Partial braking Full brake

TTC (s)

TTC gt 20 sBraking force 00 g




Figure 2 AEB logic

Camerasensor

Radarsensor

Lane information

Relative distance

TTC-based collision detection

Ego vehicleDisplay

warning vehicle

Collisionavoidance

system(AEB system)

Relative speed

Relative distance

Figure 3 Block diagram of vehicle-mounted-sensor-based AEBsystem

control were conducted by grafting ADAS with adaptivecruise control and realizing intervehicle communication [8ndash11] Consequently a presentation on the usability of the newconceptual AEB system that employs V2V communicationwas required

Therefore in this study to overcome the aforemen-tioned limitations of vehicle-mounted-sensor-based systemswe propose a new conceptual AEB system that employsV2V communication along with environment recognition

sensors In addition the usability of V2V communicationwascomparedwith that of vehicle-mounted sensors bymodifyingan existing vehicle-mounted-sensor-based AEB system toincorporate V2V communication

2 AEB System Design

21 AEB System Analysis Model As shown in Figure 1 foranalyzing the usability ofV2Vcommunication in comparisonwith that of vehicle-mounted sensors a detailed modelincluding various sensors and modules the driving andcommunication environment and vehicle dynamic charac-teristics was required Therefore in this study PreScan acommercial simulation code was used for modeling wirelesscommunication modules high-precision location measure-ment systems the driving and communication environmentand the radar and camera sensors installed in a vehicle Inaddition to determine the dynamic characteristics of a uservehicle equipped with the AEB system a full car modelwith multiple degrees of freedom was generated in CarSima vehicle dynamic behavior simulation software applicationand used Finally after interfacing PreScan and CarSimthrough MATLABSimulink an analysis model was builtbased on a collision detection system AEB logic and thesetting of a given scenario


Camerasensor

Positioningsystem


Kalman filter

In-vehiclesensor

Lane information

Position heading


and velocity



Ego vehicle


Estimated position

Headingvelocity and

Collisionavoidance

system(AEB system)





Quadrant 1




rear left Vehicle 1

Ego vehicle

Vehicle 2

Relative distance1


(xego yego) 1205791

Relative 1205791

(x1 y1)

x1

x2



0∘ relative angle

1205792

(x2 y2)

egorsquos lateral


y-axis of








-axis of

egorsquos lateral


-axis of


E90∘

N0∘

W270∘

S180∘

(x998400 y998400 )

120593998400

x

x

x

x

998400y

y

y

y

998400

120593 = 0∘

120593 = 0∘


120593 =120593998400∘

120593=120593 998400∘







Egovehicle




+120579r∘


+120579f∘

120579f∘

(90∘ sim (180∘ minus 120579r∘)) (120579f

∘ sim 0∘)


∘))




(1)







Parameter









Parameter









Data fusion




Start

Yes

Collision warning


End

No




1 23


Ego20m 75m

visibility 50m






0 2 4 6 8 10 12 14 16 1800

02

04

06

08

10

Col

lisio

n de

tect

ion


Time (s)

0 2 4 6 8 10 12 14 16 18

0

2

4

6

8

10

12

14

16

18

20

TTCx

Time (s)

(c) TTCx

0 2 4 6 8 10 12 14 16 18

00A

x (g

)

Time (s)

minus10

minus08

minus06

minus04

minus02


0 2 4 6 8 10 12 14 16 180

50

100

150

200

250

300

350

400

Relat

ive d

istan

ce (m

)

Time (s)


0 2 4 6 8 10 12 14 16 180

2

4

6

8

10

12

14

16

18

20

Relat

ive s

peed

(ms

)

Time (s)

(f) Relative speed





[1199091015840

1199101015840] = [


119909

119910] (2)







Blind zone








00 02 04 06 08 1000

02

04

06

08

10


0 2 4 6 8 10 12 14 16 180

2

4

6

8

10

12

14

16

18

20

TTC

x

Time (s)

Vehicle 2 Vehicle 1

(c) TTCx

0 2 4 6 8 10 12 14 16 18

00

Ax

(g)

Time (s)

minus10

minus08

minus06

minus04

minus02


0 2 4 6 8 10 12 14 16 180

50

100

150

200

250

300

350

400

Rela

tive

dist

ance

(m)


Vehicle 3


0 2 4 6 8 10 12 14 16 18

0

2

4

6

8

10

12

14

16

18

20

Rela

tive

spee

d (m

s)

Time (s)

Vehicle 1 Vehicle 2

Vehicle 3

(f) Relative speed

Col

lisio

n de

tect

ion




0 2 4 6 8 10 12 14 16 18

2

3

4

5

6

7

War

ning

zone

Time (s)

1

Vehicle 1 Vehicle 2

Vehicle 3




4 Conclusions






Acknowledgments


References



























Volume 2014




Journal of











Journal of


Function Spaces






Algebra










Camerasensor

Positioningsystem


Kalman filter

In-vehiclesensor

Lane information

Position heading


and velocity



Ego vehicle


Estimated position

Headingvelocity and

Collisionavoidance

system(AEB system)





Quadrant 1




rear left Vehicle 1

Ego vehicle

Vehicle 2

Relative distance1


(xego yego) 1205791

Relative 1205791

(x1 y1)

x1

x2



0∘ relative angle

1205792

(x2 y2)

egorsquos lateral


y-axis of








-axis of

egorsquos lateral


-axis of


E90∘

N0∘

W270∘

S180∘

(x998400 y998400 )

120593998400

x

x

x

x

998400y

y

y

y

998400

120593 = 0∘

120593 = 0∘


120593 =120593998400∘

120593=120593 998400∘







Egovehicle




+120579r∘


+120579f∘

120579f∘

(90∘ sim (180∘ minus 120579r∘)) (120579f

∘ sim 0∘)


∘))




(1)







Parameter









Parameter









Data fusion




Start

Yes

Collision warning


End

No




1 23


Ego20m 75m

visibility 50m






0 2 4 6 8 10 12 14 16 1800

02

04

06

08

10

Col

lisio

n de

tect

ion


Time (s)

0 2 4 6 8 10 12 14 16 18

0

2

4

6

8

10

12

14

16

18

20

TTCx

Time (s)

(c) TTCx

0 2 4 6 8 10 12 14 16 18

00A

x (g

)

Time (s)

minus10

minus08

minus06

minus04

minus02


0 2 4 6 8 10 12 14 16 180

50

100

150

200

250

300

350

400

Relat

ive d

istan

ce (m

)

Time (s)


0 2 4 6 8 10 12 14 16 180

2

4

6

8

10

12

14

16

18

20

Relat

ive s

peed

(ms

)

Time (s)

(f) Relative speed





[1199091015840

1199101015840] = [


119909

119910] (2)







Blind zone








00 02 04 06 08 1000

02

04

06

08

10


0 2 4 6 8 10 12 14 16 180

2

4

6

8

10

12

14

16

18

20

TTC

x

Time (s)

Vehicle 2 Vehicle 1

(c) TTCx

0 2 4 6 8 10 12 14 16 18

00

Ax

(g)

Time (s)

minus10

minus08

minus06

minus04

minus02


0 2 4 6 8 10 12 14 16 180

50

100

150

200

250

300

350

400

Rela

tive

dist

ance

(m)


Vehicle 3


0 2 4 6 8 10 12 14 16 18

0

2

4

6

8

10

12

14

16

18

20

Rela

tive

spee

d (m

s)

Time (s)

Vehicle 1 Vehicle 2

Vehicle 3

(f) Relative speed

Col

lisio

n de

tect

ion




0 2 4 6 8 10 12 14 16 18

2

3

4

5

6

7

War

ning

zone

Time (s)

1

Vehicle 1 Vehicle 2

Vehicle 3




4 Conclusions






Acknowledgments


References



























Volume 2014




Journal of











Journal of


Function Spaces






Algebra











-axis of

egorsquos lateral


-axis of


E90∘

N0∘

W270∘

S180∘

(x998400 y998400 )

120593998400

x

x

x

x

998400y

y

y

y

998400

120593 = 0∘

120593 = 0∘


120593 =120593998400∘

120593=120593 998400∘







Egovehicle




+120579r∘


+120579f∘

120579f∘

(90∘ sim (180∘ minus 120579r∘)) (120579f

∘ sim 0∘)


∘))




(1)







Parameter









Parameter









Data fusion




Start

Yes

Collision warning


End

No




1 23


Ego20m 75m

visibility 50m






0 2 4 6 8 10 12 14 16 1800

02

04

06

08

10

Col

lisio

n de

tect

ion


Time (s)

0 2 4 6 8 10 12 14 16 18

0

2

4

6

8

10

12

14

16

18

20

TTCx

Time (s)

(c) TTCx

0 2 4 6 8 10 12 14 16 18

00A

x (g

)

Time (s)

minus10

minus08

minus06

minus04

minus02


0 2 4 6 8 10 12 14 16 180

50

100

150

200

250

300

350

400

Relat

ive d

istan

ce (m

)

Time (s)


0 2 4 6 8 10 12 14 16 180

2

4

6

8

10

12

14

16

18

20

Relat

ive s

peed

(ms

)

Time (s)

(f) Relative speed





[1199091015840

1199101015840] = [


119909

119910] (2)







Blind zone








00 02 04 06 08 1000

02

04

06

08

10


0 2 4 6 8 10 12 14 16 180

2

4

6

8

10

12

14

16

18

20

TTC

x

Time (s)

Vehicle 2 Vehicle 1

(c) TTCx

0 2 4 6 8 10 12 14 16 18

00

Ax

(g)

Time (s)

minus10

minus08

minus06

minus04

minus02


0 2 4 6 8 10 12 14 16 180

50

100

150

200

250

300

350

400

Rela

tive

dist

ance

(m)


Vehicle 3


0 2 4 6 8 10 12 14 16 18

0

2

4

6

8

10

12

14

16

18

20

Rela

tive

spee

d (m

s)

Time (s)

Vehicle 1 Vehicle 2

Vehicle 3

(f) Relative speed

Col

lisio

n de

tect

ion




0 2 4 6 8 10 12 14 16 18

2

3

4

5

6

7

War

ning

zone

Time (s)

1

Vehicle 1 Vehicle 2

Vehicle 3




4 Conclusions






Acknowledgments


References



























Volume 2014




Journal of











Journal of


Function Spaces






Algebra











Parameter









Parameter









Data fusion




Start

Yes

Collision warning


End

No




1 23


Ego20m 75m

visibility 50m






0 2 4 6 8 10 12 14 16 1800

02

04

06

08

10

Col

lisio

n de

tect

ion


Time (s)

0 2 4 6 8 10 12 14 16 18

0

2

4

6

8

10

12

14

16

18

20

TTCx

Time (s)

(c) TTCx

0 2 4 6 8 10 12 14 16 18

00A

x (g

)

Time (s)

minus10

minus08

minus06

minus04

minus02


0 2 4 6 8 10 12 14 16 180

50

100

150

200

250

300

350

400

Relat

ive d

istan

ce (m

)

Time (s)


0 2 4 6 8 10 12 14 16 180

2

4

6

8

10

12

14

16

18

20

Relat

ive s

peed

(ms

)

Time (s)

(f) Relative speed





[1199091015840

1199101015840] = [


119909

119910] (2)







Blind zone








00 02 04 06 08 1000

02

04

06

08

10


0 2 4 6 8 10 12 14 16 180

2

4

6

8

10

12

14

16

18

20

TTC

x

Time (s)

Vehicle 2 Vehicle 1

(c) TTCx

0 2 4 6 8 10 12 14 16 18

00

Ax

(g)

Time (s)

minus10

minus08

minus06

minus04

minus02


0 2 4 6 8 10 12 14 16 180

50

100

150

200

250

300

350

400

Rela

tive

dist

ance

(m)


Vehicle 3


0 2 4 6 8 10 12 14 16 18

0

2

4

6

8

10

12

14

16

18

20

Rela

tive

spee

d (m

s)

Time (s)

Vehicle 1 Vehicle 2

Vehicle 3

(f) Relative speed

Col

lisio

n de

tect

ion




0 2 4 6 8 10 12 14 16 18

2

3

4

5

6

7

War

ning

zone

Time (s)

1

Vehicle 1 Vehicle 2

Vehicle 3




4 Conclusions






Acknowledgments


References



























Volume 2014




Journal of











Journal of


Function Spaces






Algebra











0 2 4 6 8 10 12 14 16 1800

02

04

06

08

10

Col

lisio

n de

tect

ion


Time (s)

0 2 4 6 8 10 12 14 16 18

0

2

4

6

8

10

12

14

16

18

20

TTCx

Time (s)

(c) TTCx

0 2 4 6 8 10 12 14 16 18

00A

x (g

)

Time (s)

minus10

minus08

minus06

minus04

minus02


0 2 4 6 8 10 12 14 16 180

50

100

150

200

250

300

350

400

Relat

ive d

istan

ce (m

)

Time (s)


0 2 4 6 8 10 12 14 16 180

2

4

6

8

10

12

14

16

18

20

Relat

ive s

peed

(ms

)

Time (s)

(f) Relative speed





[1199091015840

1199101015840] = [


119909

119910] (2)







Blind zone








00 02 04 06 08 1000

02

04

06

08

10


0 2 4 6 8 10 12 14 16 180

2

4

6

8

10

12

14

16

18

20

TTC

x

Time (s)

Vehicle 2 Vehicle 1

(c) TTCx

0 2 4 6 8 10 12 14 16 18

00

Ax

(g)

Time (s)

minus10

minus08

minus06

minus04

minus02


0 2 4 6 8 10 12 14 16 180

50

100

150

200

250

300

350

400

Rela

tive

dist

ance

(m)


Vehicle 3


0 2 4 6 8 10 12 14 16 18

0

2

4

6

8

10

12

14

16

18

20

Rela

tive

spee

d (m

s)

Time (s)

Vehicle 1 Vehicle 2

Vehicle 3

(f) Relative speed

Col

lisio

n de

tect

ion




0 2 4 6 8 10 12 14 16 18

2

3

4

5

6

7

War

ning

zone

Time (s)

1

Vehicle 1 Vehicle 2

Vehicle 3




4 Conclusions






Acknowledgments


References



























Volume 2014




Journal of











Journal of


Function Spaces






Algebra












[1199091015840

1199101015840] = [


119909

119910] (2)







Blind zone








00 02 04 06 08 1000

02

04

06

08

10


0 2 4 6 8 10 12 14 16 180

2

4

6

8

10

12

14

16

18

20

TTC

x

Time (s)

Vehicle 2 Vehicle 1

(c) TTCx

0 2 4 6 8 10 12 14 16 18

00

Ax

(g)

Time (s)

minus10

minus08

minus06

minus04

minus02


0 2 4 6 8 10 12 14 16 180

50

100

150

200

250

300

350

400

Rela

tive

dist

ance

(m)


Vehicle 3


0 2 4 6 8 10 12 14 16 18

0

2

4

6

8

10

12

14

16

18

20

Rela

tive

spee

d (m

s)

Time (s)

Vehicle 1 Vehicle 2

Vehicle 3

(f) Relative speed

Col

lisio

n de

tect

ion




0 2 4 6 8 10 12 14 16 18

2

3

4

5

6

7

War

ning

zone

Time (s)

1

Vehicle 1 Vehicle 2

Vehicle 3




4 Conclusions






Acknowledgments


References



























Volume 2014




Journal of











Journal of


Function Spaces






Algebra










00 02 04 06 08 1000

02

04

06

08

10


0 2 4 6 8 10 12 14 16 180

2

4

6

8

10

12

14

16

18

20

TTC

x

Time (s)

Vehicle 2 Vehicle 1

(c) TTCx

0 2 4 6 8 10 12 14 16 18

00

Ax

(g)

Time (s)

minus10

minus08

minus06

minus04

minus02


0 2 4 6 8 10 12 14 16 180

50

100

150

200

250

300

350

400

Rela

tive

dist

ance

(m)


Vehicle 3


0 2 4 6 8 10 12 14 16 18

0

2

4

6

8

10

12

14

16

18

20

Rela

tive

spee

d (m

s)

Time (s)

Vehicle 1 Vehicle 2

Vehicle 3

(f) Relative speed

Col

lisio

n de

tect

ion




0 2 4 6 8 10 12 14 16 18

2

3

4

5

6

7

War

ning

zone

Time (s)

1

Vehicle 1 Vehicle 2

Vehicle 3




4 Conclusions






Acknowledgments


References



























Volume 2014




Journal of











Journal of


Function Spaces






Algebra










0 2 4 6 8 10 12 14 16 18

2

3

4

5

6

7

War

ning

zone

Time (s)

1

Vehicle 1 Vehicle 2

Vehicle 3




4 Conclusions






Acknowledgments


References



























Volume 2014




Journal of











Journal of


Function Spaces






Algebra
































Volume 2014




Journal of











Journal of


Function Spaces






Algebra
















Volume 2014




Journal of











Journal of


Function Spaces






Algebra









Documents

Research Article Usability Analysis of Collision Avoidance System …downloads.hindawi.com/journals/jam/2014/951214.pdf · based on a collision detection system, AEB logic, and the