20.IJAEST-Vol-No-6-Issue-No-1-A-Novel-Dangerous-Vehicle-Detection-Protocol-For-Safety-Application-12 by ISERP ISERP

K.PREMA KUMAR et al. / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 6, Issue No. 1, 124 - 132

A Novel Dangerous Vehicle Detection Protocol For Safety Application K.PREMA KUMAR

B.M.KUSUMA KUMARI

DR.K.SRI RAMAKRISHNA

Student M.Tech

Lecturer Professor & Head of Department Department TIFAC-CORE in TELEMATICS V.R. Siddhartha Engineering College , Vijayawada, A.P-520007 premakumarkumbha@gmail.com bhupathimercy@gmail.com srk_kalva@yahoo.com

M.KANTI KIRAN Lecturer

kantikiran81@gmail.com

Abstract:- Recently, considerable research has been focused on applying ad hoc wireless networking technology to on-themove vehicles. In this paper, we focus on distributed detection of dangerous vehicles on roads and highways using the dangerous vehicle-detection protocol (DVDP) to detect drivers who violate the permitted speed limit. In DVDP, each vehicle

collects surrounding vehicles identifications (IDs) and propagates warning information (including its position, speed, time, and collected IDs). This information is then forwarded hop-by-hop using ad hoc communications. A vehicle that receives this information will start to observe its surrounding vehicles. If surrounding vehicles are identified in the received warning information, it will estimate the speed of such vehicles. If the estimated speed exceeds the permitted speed, such vehicles are

then marked as “suspected vehicles,” and the updated warning information is further propagated. By repeating this process, the suspected vehicle (where other vehicles observe the speed violation) will ultimately be marked as a “dangerous vehicle.” This judgment is then further propagated to warn others and to inform the traffic police. We evaluated the performance of DVDP using a simulator that performs both macroscopic and microscopic traffic simulation, taking into account realistic lane and speed models, mobility, position, and location errors. Simulation results revealed that DVDP’s detection probability is greater than 80% when vehicle density is above 40 vehicles/min. When vehicle density is low, deployment of relay points can help to further improve detection probability. In addition, by utilizing vehicles in opposite lanes, detection probability can be further improved. This inter-vehicular communication protocol i.e., dangerous vehicle-detection protocol useful for to collect

the fine from the driver who exceeds the permitted speed on highways and to warn other drivers from accidents. Keywords : Mobile ad-hoc networks , mobile communication , vehicular mobility and vehicle-to-vehicle communication.

I . INTRODUCTION

Vehicular Ad Hoc Networks (VANETs) have recently

attracted the attention of

network congestion , performance analysis, privacy and

researchers , automotive

security[5] .In VANETS communications between nodes are

industry, and governments , for their potential of improving

direct ,i.e. without relying on any dedicated infrastructure

safety of the surrounding environment through infrastructure

,contrary to earlier vehicular networks Although it is self-

less ,vehicle to-vehicle(V2V) wireless communications The

organized and easily to deploy, the infrastructure less

vision for VANETs includes applications such as route

vehicular network

planning, road safety, e-commerce , entertainment in for

be tackled before real implementations. For instance, to allow

VANETs includes applications such as route planning, road

communications between nodes (vehicles) which are out of

safety,

[2],

the power range of each other some other intermediaries

cooperative driver assistance, sharing traffic and road

should act as routers, to remedy the lack of dedicated routers.

conditions for smooth traffic flow, user interactions,

Thus, a distributed routing protocol needs to be employed. In

information services, etc. VANET raises several interesting

the European Union Seventh Framework Program me

issues in regard to media access control (MAC), data

SAFESPOT project [11],[12] and the Japanese Advanced

aggregation, data validation , data dissemination , routing ,

Road

e-commerce

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entertainment

vehicles

introduces many challenges that should

Transportation

Systems

(ARTS)

project,

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K.PREMA KUMAR et al. / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 6, Issue No. 1, 124 - 132

propagates warning information including their position,

vehicle-to-vehicle (V2V) and vehicle to- roadside (V2I) [9]

speed, time, and collected IDs. When a vehicle receives

[10] communications are actively being studied .In fact, V2V

warning information, the vehicle calculates the distance

is considered useful for localized emergency and respond

between the current position and the position where warning

cases, while V2I is viewed as the long-haul pipe for

information was generated. If the distance is shorter than a

noncritical information push and pull. In the ARTS project,

certain amount, the vehicle will forward the warning

there are several research projects on cooperatively detecting

information to preceding vehicles as soon as possible.

vehicles

corners,

Otherwise , the vehicle that receives the warning information

intersections, vehicles in the wrong lane, vehicles travelling at

starts to observe its surrounding vehicles. When it finds

extremely dangerous speeds, and vehicles wrongly stopping or

vehicles whose IDs are included in its received warning

running on a motorway shoulder. In this paper, we focus on

information, it estimates the speed of those vehicles. If the

the use of ad- hoc communications to detect dangerous

estimated speed exceeds the permitted speed limit ,those

vehicles on roads and highways [6],[7],[8] .

Reckless and

vehicles are considered as ― suspected vehicles,‖ and the

careless driving can endanger the lives of other drivers and

updated warning information is then further propagated to

passengers on roads and highways. Increasingly, many

vehicles ahead. By repeating this process, the suspected

accidents can be avoided if such dangerous drivers are

vehicle where multiple other vehicles witnessed the speed

detected early and other drivers are warned [14][15]. In most

violation will ultimately be marked as a ― dangerous vehicle.‖

from

approaching

ramps,

invisible

roads nowadays, cameras and speed detectors are used to

intelligent transportation system. technologies based on

monitor and identify drivers who had exceeded the permitted speed limit on roads and highways. This is the primitive approach, and there are limitations. If the drivers slow down

before the speed detectors, they will not be detected, even

though they had exceeded the permitted speed earlier. Additionally, bad weather can distort the quality of image captured

by cameras.

Currently,

there

are

various

interpretations and definitions of dangerous drivers. Reckless drivers are those who are negligent. For example, they do not

give signals when overtaking other vehicles, or they follow too closely to vehicles ahead of them. Fatigue drivers are those who are overpowered by physical and mental stress.

They fell asleep while driving, creating a dangerous situation

Fig. 1. Overview of the proposed dangerous-vehicle-detection mechanism

on roads and highways, resulting in accidents and Collisions 19] . Aggressive drivers are those who drive and overtake

II. DANGEROUS VEHICLE DETECTION PROTOCOL

others dangerously, often speeding, and do not give warning

Current methods of deploying speed detectors at

or signals to others. They create catastrophic accidents, often

fixed points are inadequate for detecting certain kinds of

involving fatal injuries and loss of lives. In this paper, we

dangerous vehicles. For example, if a dangerous vehicle

focus our research on the detection of drivers who violate the

exceeding the permitted speed limit knows the locations of

speed limits on roads and highways [16],[18] . We used the

those speed detectors, it might slow down before the speed

dangerous-vehicle-detection protocol (DVDP) to detect

detectors, thereby avoiding detection. In this paper, we

drivers who violate the perm permitted speed limit .Here ,we

propose DVDP to detect dangerous vehicle autonomously. Its

assume that a kind of next-generation license plate number

operation is discussed in the following section.

system is used, where each vehicle disseminates its own

A . Assumption for Proposed Protocol:

vehicle identification (ID) in a regular interval. In DVDP, each

vehicle

collects

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surrounding

vehicles’

IDs

and

Here, we assume a kind of next-generation license plate system. We assume that each vehicle broadcasts its

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K.PREMA KUMAR et al. / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 6, Issue No. 1, 124 - 132

denotes the suspected level of a monitoring vehicle Vid .When

is 40 m. It is also assumed that those vehicles have wireless

vehicle Vid estimates the speed of each surrounding vehicle

communication facilities so that they can transmit and receive

Vid , if it finds that Vid exceeds the permitted speed limit, i.e.,

data. For inter-vehicular communications, we use the

if the estimated speed vk in TRid exceeds the permitted speed

IEEE802.11 independent basic service set (IBSS) [3], [4]. Our

limit, the vehicle Vid sets {TRid , 1 } into its suspected vehicle

protocol is an application layer protocol running over user

information Sid. Also, if vehicle Vid finds again that Vid in Sid

datagram

and

still exceeds the permitted speed limit, the suspected level is

wireless

incremented by one, i.e., it is incremented as { TRid , levelid +

communication range is 200 m and that each vehicle knows its

1} .The vehicles in Sid correspond to questionable vehicles,

precise position and time from its global positioning system

and they are continuously monitored by other vehicles . If the

(GPS) receiver. Different radios are used for transmitting data

suspected level reaches a predefined threshold N , { TRid ,

and vehicle IDs. In DVDP , each vehicle collects vehicles’ Ids

levelid } is recorded into the dangerous vehicle information

disseminated from surrounding vehicles and forwards them to

Did. The data structure of dangerous vehicle information Did

preceding vehicles as warning information (see Fig. 1). Note

is the same as the suspected vehicle information Sid. If

that DVDP can work well even if dangerous vehicles do not

preceding vehicles receive the dangerous vehicle information

execute DVDP although we assume that all vehicles including

from vehicles in the rear, they can continuously monitor the

dangerous

dissemination of vehicle IDs of those vehicles. If they find

protocol/

IEEE802.11.

Here,

vehicles

Internet we

protocol

assume

broadcast

(UDP/IP)

that

their

the

vehicle

ID’s

periodically[1].

vehicle ID periodically, and that the vehicle ID’s radio range

B . DVDP Operation

such dissemination of vehicle IDs, they can recognize the approach of dangerous vehicles in advance. Hence, this can

Let Vid denotes the vehicle whose vehicle ID is id.

enhance the safety of preceding vehicles.

Warning information Wid of Vid contains the following four

types of information ( Wid = < Oid , Mid, Sid , Did > ): 1) observing vehicle information Oid; 2) monitoring vehicle information Mid; 3) suspected vehicle information Sid; and 4) dangerous vehicle information Did. The observing vehicle

information Oid is represented as 4- tuple ( Vid, tid, pid, dirid

) , where pid denotes the position of the vehicle Vid at time tid, and dirid denotes the direction in which it is moving. When other vehicles receive the warning information, they can recognize where the vehicle Vid is running at time tid and

to which direction it is moving from pid and dirid, respectively.

Fig.2 Dangerous vehicle speed estimation

The monitoring vehicle information Mid is represented as a set { TRid } of monitoring vehicles’ moving traces. The

C. Detection Approach

moving trace TRid of each monitored vehicle Vid is

In DVDP, each vehicle executes the following two

represented as a pair {trid, id } of a list of positions trid and its

processes in parallel. 1. forwarding process and 2.Observing

vehicle’s ID

process.

id_.

Here, trid denotes a list of positions { ( t1, p1,

The

forwarding

process

relays

the

warning

v1),( t2, p2, v2 ), . . . , ( tk, pk, vk ) }, where pi and vi denote the

information sent from rear vehicles to preceding vehicles. It

estimated position and speed of the monitored vehicle Vid_ at

also relays the warning information of oncoming vehicles so

time ti, respectively. The estimated speed vi is calculated by

that they can be used for vehicles running in opposing lanes

comparing its current position and time with its preceding

.The observing process, however, monitors dissemination of

position and time .

vehicle IDs from surrounding vehicles and estimates the speed

The suspected vehicle information Sid is represented as a

of surrounding vehicles using the warning information

set of suspected vehicles Sid = { TRid, levelid }, where levelid_

obtained in the forwarding process. The forwarding process

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b) If id’ is not included in the observing list, then construct its

be forwarded. Likewise, the observing process has an

moving trace << tcur, pcur, vcur> , id’>, and add it to the

observing list for keeping vehicle IDs to be monitored.

forwarding list.

1). Forwarding Process: The forwarding process is executed

c) If id_ is included in the observing list and if <<t1, p1, v1>,

as follows.

<t2, p2, v2>, . . . , <tk, pk, vk>, id’> is recorded as a moving

a) If a vehicle Vid receives a warning information Wid, it

trace, then estimate its current speed Vcur as follows:

compares its current position of Vid with the position of the

Vcur = d(pk, pcur) − 2R /tcur – tk

sender vehicle (Vid ) recorded in Wid. It also compares the

where d(p1, p2) denotes the distance between p1 and p2.

moving direction.

d) Let Vlimit denote the permitted speed limit. If Vcur > Vlimit,

b) If Wid is sent from an oncoming vehicle running on the

then increment the suspected level lid , and put it on the

opposite lanes, then add Wid into the forwarding list, and go to

forwarding list. Let L denote a threshold. If lid > L, then treat

step 5).

the corresponding vehicle as a dangerous one, and put it on the

c) If Wid is sent from a front vehicle running in the same

forwarding list.

direction, then discard it, and repeat from step 1).

e) For a predefined speed Vsend, if Vsend ≤ Vcur ≤ Vlimit, then

d) If Wid is sent from a rear vehicle running in the same

retain the suspected level lid, and add the corresponding

direction, then do the following.

warning information on the forwarding list.

1) For each moving trace <trid’’, id‖> in the monitoring

f) Otherwise (Vcur < Vsend), remove the target vehicle Vid from

vehicle information Mid_ = {<trid‖, id‖>} of Wid ,if the

the observing list.

has a forwarding list for keeping the warning information to

distance between the latest position of the moving trace <trid‖,

Here, we assume that the observing list is periodically

id‖> and the current position is less than a fixed distance Dmin,

updated and old information is discarded. For each

then put the moving trace <trid‖, id‖> into the forwarding list.

information whose latest timestamp is older than a fixed

Repeat this process for all moving traces in Mid’.

period Top, observation of such vehicles is no longer

2) Else, if the distance is larger than or equal to Dmin, then add

necessary, and the old information is discarded. In each

<trid, id> into the observing list.

moving trace TRid_ = <{<t1, p1, v1>, <t2, p2, v2>, . . . , <tk, pk, vk>}, Vid’> , a new list <tcur, pcur, vcur> is added only when the

contents as a new warning message, and remove them from

suspected level is incremented. Therefore, the maximum

the list.

length of the list is N + 1, where N is the predefined threshold

e) If the forwarding list is not empty, then disseminate its

The speed check of suspected vehicles will begin after

for deciding whether a suspected vehicle is treated as a

the warning information is propagated over the fixed distance

dangerous vehicle. If each<ti, pi, vi> is given as 20-B data,

Dmin. When the vehicular distance is short, vehicles driving in

and if Vid’ is given as 4-B data, the size of a moving trace TRid’ is 20 * N + 24 B .For neighboring vehicles running with

the warning information. Here, if the differential angle

similar speeds, their suspected levels are zero. In such a case,

(absolute value) between the moving direction described in the

the required data size is 24 B since N = 0. Although the size of

received warning information and the current vehicle moving

warning information varies depending on how many vehicles’

direction is less than π/2, both vehicles are considered to be

information should be forwarded, in our experiments, the sizes

moving in the same direction. Else, they are said to be moving

of the warning information are smaller than 1 kB in most

in opposite directions.

cases. The number of transmitted warning information per

2 .Observing Process:Each vehicle Vid executes the following

second within the wireless communication range of each

front may encounter the suspected vehicle before they receive

observing process (see Fig. 2). a) If vehicle Vid finds dissemination of a vehicle ID id’ from its surrounding vehicle Vid’ , then note its own current position pcur, speed vcur, and time tcur.

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vehicle is at most 20–50, even for rather congested road conditions. This amount is enough for transmitting data in IEEE 802.11p environments .When estimating the speed of a surrounding vehicle, we cannot find its exact position.

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In Fig. 2, if R is the wireless communication range, the real

successfully reproduce vehicular density and fluctuations of

position of an observed vehicle might be at most R meters

real traffic flows.

.away from the position of its monitoring vehicle. Therefore, if

A. Speed Model:

the distance between two monitoring vehicles is X meters, the

engineering, it is known that, in general, each vehicle’s speed

actual distance X_ between two positions of vehicle VX

V can be modeled by the following Green shields’ function

satisfies X − 2R ≤ X_ ≤ X + 2R. Therefore, we use the

[17]:

According to the theory of traffic

minimum distance X − 2R as the distance between two positions of vehicle VX when estimating its speed. Since X −

V = VMAX ×(1 – K/KMAX )

- (1)

2R is used, the estimated speed is slower than the actual speed. This avoids cases where non dangerous vehicles are

V speed of the target vehicle;

detected as dangerous vehicles.

K density of vehicles around the target vehicle;

The warning information is also transmitted when the

VMAX maximum speed at which vehicles can run in case that K = 0;

speed is less than the permitted speed limit Vlimit. If the

KMAX saturated density of vehicles when their speeds become

warning information is not sent when the estimated speed is

lower than Vlimit, dangerous vehicles might not be detected if

Here, we assume that the ratio K/KMAX is roughly proportional

they temporarily slow down. By relaying warning information

to the ratio DMIN/D .Then, we arrive at

of vehicles whose speeds are faster than Vsend, we avoid misdetection dangerous vehicles. Hence, we refer Vsend as the forwarding speed.

estimated speed is faster than a fixed speed Vsend, even if the

V = VMAX ×(1 − DMIN /D)

(2)

where D is the distance to the preceding vehicle, and DMIN is

III . REALISTIC VEHICULAR MOBILITY

the minimum distance between two vehicles.

In the evaluation of inter-vehicular communication, it is desirable that realistic vehicular traffic flows are considered. If

IV.SIMULATION ENVIRONMENT

inter-vehicular communication is used for estimating the

A. Simulation environment: we evaluated the performance of our proposed DVDP on

jam form a line with similar distances, i.e., they have uniform

metropolitan highways. The common simulation condition is

arrangement. If inter-vehicular communication is used for

given as follows. We assume that the legally allowed highway

avoiding collisions at intersections, one can assume that a

speed is 100 km/h and that each normal vehicular speed is set

message informing the approach of a vehicle can reach

at 100 ±5 km/h . We vary the vehicular density from 5 to 55

vehicles on its crossing road by at most a few hops. In such

vehicles/min. Each highway has two or three lanes, and there

cases , performance of inter-vehicular communication for real

is almost no traffic congestion for the given vehicular density

traffic might be similar to simulation results based on artificial

.Here, vehicles with an average speed exceeding 130 km/h are

macroscopic traffic flow models. However, to identify moving

classified as dangerous vehicles. The number of dangerous

traces of dangerous vehicles and estimate when and how

vehicles is set to 9.For inter-vehicular communications, we

dangerous vehicles exceed the permitted speed using inter-

use the IEEE802.11 IBSS [7], [8]. Our protocol is an

vehicular communication, one must carefully reproduce real

application layer protocol running over UDP/IP and

traffic flows on highways for considerable time periods. This

IEEE802.11. The warning information dissemination range is

requires a realistic vehicular mobility model. In this section,

200 m, and the vehicle ID’s radio range is 40 m. Different

we consider vehicular mobility by taking into account realistic

radios are used for sending data and vehicle IDs. For wireless

speed model and lane selection/change model. We have

communications, it is known that the probability of successful

extracted specific characteristics of real vehicular mobility

reception of each packet depends on the inter-vehicular

from several vehicular positions on Google Earth , and we

distance and that it decreases suddenly when the distance

have shown that our vehicular mobility model can

becomes larger [20]. Here, we approximate that if packet

duration of a traffic jam, one can assume that vehicles in the

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collision does not occur, the correct reception probability of

speed between the normal vehicles and dangerous ones is at

the warning information, which depends on the inter-vehicular

most 100 km/h (28 m/s), and it is less than the radio range of

distance, follows the expression 1 â&#x2C6;&#x2019; (x/D)2, where x denotes

vehicle IDs (40 m), each vehicle can receive the vehicle ID of

the inter-vehicular distance, and D denotes the data

every passing vehicle with a high probability, even if the

dissemination range. If packet collision occurs, then we say

passing vehicleâ&#x20AC;&#x2122;s speed is very fast .In our simulation, we will

that collided packets are lost . We assume all vehicle IDs

evaluate the performance of DVDP in scenarios with and

from surrounding vehicles can be received at the same

without relay points. Our simulation results are discussed

probability of 60% for simplicity of discussion. Here, every

below

vehicle emits its vehicle ID every second. Since the relative

Fig .4. Correlation between Dmin and detection probability

Fig.3. Suspected level versus Dmin

Fig .5. Detection probability with location and time error

Fig. 6. Detection probability with the help of vehicles in opposite lanes

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and the estimated speed V becomes large. This makes the detection probability low since the estimated speed is rather slower than the actual speed. On the other hand, if Dmin is very long, the warning information is often lost due to the absence of forwarding vehicles. As shown in Fig. 4, in our experiments ,if Dmin is 300 m, the detection probability is less than 40%. If it increases, i.e., if it is between 500 and 800 m, the detection probability becomes 60%. If it exceeds 1000 m, the detection probability decreases. From these results, we choose Dmin =800 m .We discuss here the error between the estimated speed and the actual speed caused by position and time errors. Here, we assume that the position error is −10 < dx < 10 m

and that the time error of GPS is −0.5 < tx < 0.5 s. The position and time errors are present in both front- and rearobserving vehicles. Hence, the estimated speed V _ is

Fig.7. Detection probability versus forwarding speed.

calculated as V In

the

forwarding

process,

= X + 2dx − 2R/T + 2tx. Here, X and T

denote the exact location and time. By considering the range

B. Dmin Versus Suspected Level Versus Detection Probability

when

warning

of dx and tx from the assumption, the range of V _ is (X − 20

information is sent from a rear vehicle to its preceding vehicle,

− 2R/T + 1) < Vi < (X + 20 − 2R/T − 1). Fig.5 denotes the

if the distance between the two vehicles is shorter than Dmin,

detection probability of dangerous vehicles with location and

the preceding vehicle just forwards the received warning

time errors. This evaluation was done with the dangerous

information to its preceding

vehicle’s speed at 150 km/h and Dmin at 800 m. As shown,

Vehicle. The speed check is

there is no

exceeds Dmin. Here, we have evaluated the influence of Dmin

probabilities with and without errors. Hence, our proposed

on suspected levels of dangerous vehicles. The two evaluation

DVDP can work well even if vehicles do not have exact

parameters are listed as follows:

location and time information.

1) Vehicle density = 31.7 vehicles passing in a minute;

D. Influences by Vehicles From Opposite Lanes :

carried out only if the distance between the two vehicles

2) Dangerous vehicles speed = 150 km/h.

So far, we have focused on vehicles running in the same direction. Here, we utilize vehicles in the opposite lanes to

Fig. 3 shows the increase in suspected levels for different

major difference between the detection

values of Dmin. As Dmin decreases, the suspected level

relay warning information. This can further improve detection

increases quickly . As Dmin increases ,the suspected level

probability . Fig. 6.denotes the detection probability for the

increases with a lower gradient. While a shorter Dmin increases

following four cases.

the suspected levels quickly, too short a distance can result in

1) Observing and forwarding vehicles are the only vehicles

the failure of detecting dangerous vehicles.

driving in the same direction with the dangerous vehicles.

C. Position and Time Errors:

2) Observing vehicles are vehicles driving in the same

In our DVDP, the speed of each observed vehicle is

direction with the dangerous vehicles and forwarding vehicles

checked when it is recognized that the vehicle moves Dmin

are vehicles driving in both lane directions.

meters. As shown in Fig.2. Suspected Level versus Dmin

3) The role of vehicles is the same as the first case, but

meters, the actual moving distance X of each observed vehicle

additional relay points are set per 1 km by the roadside.

might be 2+R meters longer than the estimated distance X. If

4) The role of vehicles is the same as the second case, but

it takes T seconds to move Dmin meters, the actual speed might

relay points are set per 1 km by the roadside.

be (Dmin + 2 * R)/T , although the estimated speed is Dmin/T. If

As shown in Fig.6, with vehicles running in the same lane and

Dmin is very short, the difference between the actual speed Vi

direction, as vehicle density increases, detection probability

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K.PREMA KUMAR et al. / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 6, Issue No. 1, 124 - 132

also increases, reaching above 80% beyond 40 vehicles/min.

models, speed of dangerous vehicles, forwarding speed, relay

However, with the inclusion of vehicles from opposite lanes ,

points, and vehicles in opposite lanes . Simulation results

detection probability is greatly improved

revealed that detection probability for realistic vehicular

.E . Detection Probability Versus forwarding Speed:

mobility is less than that of the macroscopic case. Increasing

Next, we focus on the forwarding speed Vsend. If a

forwarding speed reduces detection probability

dangerous vehicle slows down temporarily, the estimated

observed that the deployment of relay points increases

speed might temporally become less than the speed limit Vlimit

detection probability. Furthermore, the higher

(even if the average speed exceeds the speed limit Vlimit).

speed of dangerous vehicles, the higher the detection

Therefore, to detect such a case, we introduce the forwarding

probability. Exploiting vehicles in the opposite lanes to relay

speed Vsend , whose value is less than the speed limit Vlimit,

warning

and the warning information is forwarded when the estimated

probability. From our results, it is clear that distributed

speed exceeds Vsend .When the estimated speed is less than

detection of dangerous vehicles is possible using ad hoc

Vlimit and greater than Vsend , the suspecting level of the

sensing ,communications ,and relay points.

information

can

further

the average

improve

detection

information must continue to be forwarded.

We evaluate

warning information is not increased, and the warning VI.REFERENCES:

detection probability by varying the forwarding speed Vsend. In Fig.7, we show the detection probability under various

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