LANDMARK-BASED CAR NAVIGATION WITH OVERTAKE

CAPABILITY IN MULTI-AGENT ENVIRONMENTS

Sirvan Khalighi

, Somayeh Maabi

, Mercedeh Sanjabi

and Ali Jahanian

Department of Electronic and Computer Eng., Islamic Azad University, Qazvin Branch, Qazvin, Iran

Department of Electronic and Computer Eng., Shahid Beheshti University, Tehran, Iran

Keywords: Landmark-based car navigation, Wireless sensor networks (WSN), Multi-agent environment.

Abstract: Intelligent car navigation systems are planned to assist humans and route them automatically in the roads

with sufficient security and correctness. Landmark-based car navigation is a widely used technique in

automotive and robot navigation. In this paper, we improved a wireless landmark-based car navigation

(WLCN) algorithm to operate in multi-agent (MA) environments. The extended navigation algorithm allows

the cars to overtake in uni-directional real roads. Overtaking is based on the information which the cars send

to each other in the road. According to this information and using a related algorithm, cars traverse each

other. Analysis of accuracy and efficiency in various states, real-time RISC-based embedded system

especially for high speed movements in real roads show that, the cars are navigated easily and reliable in

multi-agent environments and they can successfully do overtake. In addition to reliable navigating,

calculation cost of the algorithm is acceptable for real world scenarios.

1 INTRODUCTION

Automatic car navigation (ACN) system is regarded

as one of the best kinds of offering solutions in

intelligent transportation systems (ITS). The ACN

systems are capable of doing some of the tasks

(reading the maps, determining the best routes and

etc.), that were performed by the driver. Recently,

GPS and digital road maps are used for land vehicle

navigation systems. The main drawback of using

GPS is false positioning, due to the imprecise

receivers and outdated maps. Therefore, GPS need

more complicated map matching algorithms to state

the vehicle location and navigation (Taghipour,

Taghipour, 2008), which lonely can't be reliable for

ACN. Most of the recent ACN algorithms are based

on machine vision and artificial intelligence

algorithms such as ant colony, neural network and

etc. (Yashikawa, Otani, 2010). (Wu et al., 2009)

developed some prototypes for landmark-based car

navigation using a full windshield head-up display

(FWD) system. They used computer vision methods

to correct distortion of FWD projection on the

windshield. (Yashikawa, Otani, 2010) obtained a

new routing algorithm to route on a graph

embedding of the map. They proposed a combined

method that integrates Tabu search and Ant colony

optimization. This hybrid technique could find the

shortest route when the blind alley existed in the

map. Their searching algorithm is comparable with

Dijkstra algorithm; But Dijkstra algorithm may not

be the best solution for drivers because each driver

has own definition in choosing the best route.

Furthermore genetic algorithm is widely used to

solve routing search and optimization problems.

Kim (Kim et al., 2009) considered the multi-

objective mathematical formulation for ACN

systems in real roads. Their method searched the

road's map to solve the problem that involves the

fuel cost in traffic congestion, the regulation of

traffic, and the weather, etc. Hashing method was

used to have a suitable selection in multi objectives

route problems. In another work (Taghipour,

Taghipour, 2008), proposed a correct mapping from

GPS on the road network parameters. Their results

show that the proposed algorithms can be effectively

used for map matching. The algorithm used only

location of the vehicle and vehicle speed information

and database of the road network. Another car

navigation system which communicated with each

other through wireless LAN was developed by

(Hiraishi et al., 1999). The system used the traffic

information from the other vehicles to perform the

234

Khalighi S., Maabi S., Sanjabi M. and Jahanian A..

LANDMARK-BASED CAR NAVIGATION WITH OVERTAKE CAPABILITY IN MULTI-AGENT ENVIRONMENTS.

DOI: 10.5220/0003752502340239

In Proceedings of the 4th International Conference on Agents and Artiﬁcial Intelligence (ICAART-2012), pages 234-239

ISBN: 978-989-8425-96-6

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

time-constrained search dynamically, yielding that

the system can generate a new route to avoid the

congestion. Significant developments and technical

trends in the area of navigation systems were

reviewed by (Hasan et al., 2009). They evaluated

integration systems for obtaining a reliable and

accurate navigation solution.

Analysis of previous works show the vision methods

are more complex and time consume thus they are

not suitable enough for real time systems. In this

situation, other options such as GPS, digital roads

and landmark-based maps should be explored. In

addition, using new communicating technologies

such as Zigbee, Wi-Fi and Wi-Max enables efficient

inter-communication in a multi-agent environment.

(Sanjabi et al., 2009) obtained a landmark-based car

navigation algorithm to route cars in the roads which

are equipped with Wi-Fi landmarks. A map

matching method has been used for navigating in the

roads without branches. The nearest visited

landmark was selected as free line. The next position

will be chosen with arithmetic formulas on this free

line. In another work we (Sanjabi et al., 2009)

discussed on navigating in multi branches routes.

Every branch has its group landmarks, hence the

route will be chosen according to its group

landmarks IDs and car will be navigated in this route

to reach the destination.

2 WIRELESS LANDMARKS

To make correct decisions during the agent’s

movement, agents need to know their location in the

environment (e.g. localization). Therefore, agents

must recognize the landmarks and communicate

with them to read their internal information. To

communicate between cars, there are many ways

such as vision, sensor, wireless and etc. But in this

project, due to the reasons which will mention in the

following, just wireless landmarks have been used

for navigating the agents: I) Wireless landmarks can

be detected in high speed movement with low

degree of error. II) In the recent years, wireless

technology is dramatically improved, and many

cost-effective wireless technologies (Wi-Fi, Zigbee

and Wi-Max) have been commercially available. III)

Information of wireless landmarks does not require

any pre-processing such as other kind of signal

processing. IV) Wireless landmarks make a new

framework to construct a large sensor network in

roads to navigate the cars. However, wireless

technology consists of a variety of standards such as

IEEE 802.1 (Zigbee), IEEE 802.2 (Bluetooth), IEEE

802.3 (Wi-Fi), IEEE 802.4 (Wi-Max) and some

other emerging technologies such as cognitive radio.

Due to significant features in terms of power

consumption and maximum coverage area, Wi-Fi

technology has been considered as wireless

landmarks. Furthermore, the coverage area of a Wi-

Fi landmark is about 50m, which is sufficient for

ACN (IEEE 802 standard, 2007), (the IEEE 802.11

protocol, 2008).

Figure 1: Place of landmarks in a road.

3 ACN USING WIRELESS

LANDMARKS

To navigate the cars in multi agent environment an

improvement of the WLCN algorithm (Sanjabi,

2009) is proposed. For each car, a Wi-Fi transceiver

which broadcasts the car’s position and receives the

location of the other landmarks is considered. There

are two types of wireless landmarks: Moving

landmarks are the cars and fixed landmarks are the

landmarks on two sides of the road (Figure 1). Due

to distance limitation of wireless applications,

Landmarks are assumed as Wi-Fi nodes (Wireless

local networking, 2008). The maximum distance

between each two Wi-Fi landmarks is a critical

parameter. The suitable distance between landmarks

will be evaluated in Section 5. Each Wi-Fi node

propagated two essential data: A unique ID and the

position of the node. When an agent reaches to a

landmark covered domain, its ID and position will

be visible by the moving agent. This information is

used to localize the agent and navigate it along the

road (Figure 2). The domain of each node maybe

overlapped with the other nodes depending on the

distance between the Wi-Fi nodes. As will be seen in

the succeeding sections, the overlapping area has

direct impact on the navigation algorithm.

4 WLCN-MA ALGORITHM

An algorithm is proposed to navigate the cars in a

multi-agent road where two sides of the road are

equipped with Wi-Fi sensors. The algorithm is

LANDMARK-BASED CAR NAVIGATION WITH OVERTAKE CAPABILITY IN MULTI-AGENT ENVIRONMENTS

235

Figure 2: Covering domain of each sensor node.

named wireless landmark-based car navigation in

multi-agent roads (WLCN-MA).It summarized in

the flowchart which is shown in Figure 3. More

details of each step will be described in the

following.

4.1 Inputs of WLCN-MA algorithm

Inputs of the proposed algorithm consist of four

parameters:

List of landmarks: The landmarks data extract

from the various maps and then save as a text file (It

is named file1). The algorithm reads the files and

extracts the required data.

Other cars information: There is another file

which contains information for navigating of the

other cars that is generated in the road (It is named

file2). This information will be used in overtaking

algorithm. In addition, the other cars are separately

navigated in the same map and their situation in each

time slot is dumped into a file. This file is used as

another input given to the WLCN-MA algorithm.

First position of car: Initial location of the car

at start of the road.

End points of the route: End of the route is the

position of two last landmarks which will be visible

at the end of the route. Obviously, the algorithm will

stop when visit them and process their data.

As summarized in Algorithm 1, WLCN-MA

algorithm initialized using the start position of the

agent in the route. In each iteration of the algorithm,

percepts (location of the landmarks) are extracted

from the environment (e.g. from input file). Current

map of the road is updated based on visited percepts.

Then, if there is a car in front of us (near than 50m),

ACN system decides to do overtake; Otherwise, a

free line (Figure 1) between the last visited percepts

is constructed.

Figure 3: Flowchart of WLCN-MA algorithm.

Algorithm 1: WLCN-MA

1. Read the start position.

2. While (TRUE)

3. P

= Get percept.

4. Update map.

5. If existing car in front of us then

(a) Decide to overtake,

(b) Do overtake.

else

Find free line.

[End of If structure]

6. Select the best next position (NP).

7. Move to new position.

8. If (NP is the end of route) then

Finish.

[End of If structure]

[End of While structure]

[End of WLCN-MA]

Figure 4: Select position based on a geometrical method.

ICAART 2012 - International Conference on Agents and Artificial Intelligence

236

lgorithm 2: Overtaking

1. Give the cars position.

2. Decrease our car speed.

3. Call Free-line algorithm.

4. Select new position in 1/4 right side of read.

5. Call Free-line algorithm.

6. Select new position in 1/2 right side of read.

7. Increase our car speed.

8. While (our distance is less than 30m)

Call Free-line algorithm.

Select new position in 3/4 right side of read.

[End of While structure]

9. Call Free-line algorithm.

10. Select new position in 1/2 right side of read.

11. Call Free-line algorithm.

12. Select new position in 1/4 right side of read.

13. Return to main algorithm.

[End of Overtaking]

lgorithm 3: Free-line

1. P

= Get percept.

2. Update map.

3. Find free line.

4. Return Free-line.

[End of Free-line]

After estimating the free line, the best point of the

free line is selected as the candidate point for the

next position of the car according to the distance

formula (1) (figure 4).

()( )

21 21

dxx yy=−+−

(1)

Finally, the next point is calculated and the current

position of the agent will be updated. If the new

current position is on the free line between the last

landmarks (end of the route), the algorithm is

finished. Otherwise, the algorithm goes ahead to get

the next percepts from the input file. As it can be

seen from the figure 4, the center of the circle is

right landmark of free line and its radius is d/4

where d denotes the road width. This crossing leads

to two positions but one point which is inside of the

road is the next position. It is worthwhile to consider

some criteria points in the selection of suitable next

position. The proposed algorithm can be easily

modified to consider any new metric.

The overtaking Algorithm: As it is shown in

the Figure 5-(a) and summarized in the algorithm 2,

algorithm 3, when one car (the red car) with lower

speed and inadequate distance, is visited in front of

our position (the white car), the main algorithm

decides to do overtake. Therefore, the car's speed is

decreased down to the front car’s speed (the red car),

then the car's movement is continued for one step in

1/4 right side of the road, where step means passing

states of get percept, update map, find free line and

(a)

(b)

Figure 5: (a) Steps of overtake process, (b) Overtake steps

(backing).

finally select next position. Then, the car must go to

1/2 and more to 3/4 from right side of the road in

two steps. After increasing our speed for passing the

front car, we regularly check our distance with the

red car by calling a move function. A move function

compares current time with the existing times in the

file2 and finds the closest time to extract the red

car’s position. This process is for simulating

concurrent movement of two cars. Then distance of

two cars calculates with the distance formula (1). If

we passed the red car with a suitable distance, we

back to 1/4 right side of the road, otherwise,

remaining in 3/4 one with passing just one step. For

checking the distance we need to know passed time

according to movement. This time is calculated by

=/

(2)

Where V, X and T denote the car's speed, car's

movement and current time respectively, and then is

fed into the move function (V is assumed as fixed

value). To finish the overtaking process, the distance

of our car and the red car is needed to be known.

Thus, distance of two cars calculates using move

function and the distance formula (1). For backing,

the car first goes to 1/2 and then 1/4 from right side

of road with passing two steps. The algorithm will

be finished if new position is located on or after the

end point of the route which is extracted (algorithm

2) (Figure 5-(b)).

5 EXPERIMENTAL RESULTS

The WLCN-MA algorithm has been implemented

LANDMARK-BASED CAR NAVIGATION WITH OVERTAKE CAPABILITY IN MULTI-AGENT ENVIRONMENTS

237

Table 1: Experimental results in terms of error rate.

Benchmark

map

Visible

landmarks

deviation

Error rate

(%)

16 0 16 0%

16 2 16 2%

16 1.5 16 1.5%

16 0.5 16 0.5%

Average 16 2 16 2%

on a MIPS-based embedded system to evaluate the

algorithm in real world conditions. Algorithm was

developed in C programming language and

compiled on MIPS architecture using a standard gcc

cross-compiler with optimization level O3. The

algorithm is evaluated with five road maps that are

extracted from the real maps.

Table 1 shows the experimental results of

different maps in terms of error rate. These maps are

shown in Figures 6, 7. The column of visible

landmarks shows the average number of visited

landmarks. Column NP deviation represents the

average deviation of calculated next position and

ideal position for the car. It is noted that the error-

free position is calculated manually to compute the

error rate. Furthermore Column D shows the length

of the free line and finally, the column Error rate

shows the deviation of estimated route in percent. As

can be seen from Table 1, the average number of

visible landmarks is almost 16. As the number of

visible landmarks increases, the reliability of the

method improves. Therefore, we need to choose

appropriate distance between the landmarks. The

minimum visible landmarks that are needed to

navigate are two, but some other problems such as

effect of noise, car speed, Wi-Fi limitations or even

road conditions enforce to require more than 2

landmarks in each step. The results of implementing

the WLCN-MA algorithm on MIPS-based

embedded system are shown in the Table 2. In this

table, Frequency’s column shows the operational

frequency of embedded system and Landmark

distance’s column shows the average distance

between landmarks. Column Total runtime shows

Table 2: Experimental results in terms of allowable

maximum speed.

Frequency

Landmark

distance

Total

runtime

Step

runtime

Max. possible

speed

(m/s)

300 MHZ

10 13.3 ms 4 ms 250

300 MHZ

20 13.3 ms 4 ms 500

300 MHZ

30 13.3 ms 4 ms 600

100 MHZ

10 40 ms 12 ms 83.33

100 MHZ

20 40 ms 12 ms 166.66

100 MHZ

30 40 ms 12 ms 250

Figure 6: The road No. 1 (first benchmark in Table 1).

Figure 7: The road No.2 (second benchmark in Table 1).

the total time needed for navigating the agent from

start to end of the path and Step runtime represents

the runtime for one step of the algorithm. And

finally, the column Maximum possible speed

represents the maximum speed of each car during

the navigation process. Experiments are performed

on all benchmarks with three different landmark

distances and three various operational frequencies.

As can be seen in this table, the WLCN-MA

algorithm can be used in high speeds with accessible

frequencies and very small error rate because of its

light computations.

6 CONCLUSIONS

This paper improved the pervious car navigation

algorithm based on the landmarks to work in multi-

agent environments. In fact, the overtaking

capability was the main improvement of it. Decision

of the overtaking is according to cars transactions

with each other. The experimental results show that

the proposed algorithm can be used for real-time and

high-speed agents even in low operational

frequencies. As future research, we are going to

ICAART 2012 - International Conference on Agents and Artificial Intelligence

238

consider other moving agents as obstacles using

more intelligent algorithms.

REFERENCES

A technical tutorial on the IEEE 802.11 protocol,

Available on htt://sss-mag.com/pdf/802_11tut.pdf,

2008.

Apolloni B., et. al, October 2005. Machine learning and

robot perception, Springer.

Hasan A. M., Samsudin Khairulmizam, Ramli Abd

Rahman, Azmir Raja Syamsul, and Ismaeel Salam A.,

2009. A review of navigation systems (Integration and

Algorithms), Australian Journal of Basic and Applied

Sciences, pp: 943-959

Hiraishi Hironori, Ohwada Hayato and Mizoguchi Fuinio,

1999. Intercommunicating car navigation system with

dynamic route finding, International Conference on

Intelligent Transportation Systems, pp: 284-289

"IEEE 802 standard," Available on http: //en.wikipedia.org

/wiki/ IEEE_802, 2007.

Kim Byung-Ki, Jo Jung-Bok, Kim Jong-Ryul and Gen

Mitsuo, 2009. Optimal route search in car navigation

systems by multi-objective genetic algorithms,

International Journal of Information Systems for

Logistics and Management, Vol. 4, No. 2, pp: 9-18

Olson C. F., 2002. Selecting landmarks for localization in

natural terrain, In Autonomous Robots Manufactured

in the Netherlands

Sanjabi M., Maabi S., Jahanian A. and Khalighi S., 2009.

A light-weight car navigation algorithm for high speed

agents using wireless landmarks, IEEE International

Conference on Information and Automation, pp: 1028

- 1033

Sanjabi M., Maabi S., Esmaeili Z., Jahanian A. and

Khalighi S., 2009. A landmark based navigation

system for high speed cars in the roads with branches,

International Journal of Information Acquisition, Vol.

6, No. 3

Taghipour Sara, Taghipour Ali, April 2008. An algorithm

for map matching for car navigation system, The

International Conference on Information &

Communication Technologies, pp: 7-11

Wu Wen, Blaicher Fabian, Yang Jie, Seder Thomas, Cui

Dehua, 2009. A prototype of landmark based car

navigation using a full windshield head up display

system, Workshop on Ambient media computing

"Wireless local networking based on 802.11standards,"

Available on http: //www.Wi-fi.org, 2008.

Yoshikawa Masaya, Otani Kazuo, 2010. Ant colony

optimization routing algorithm with Tabu search,

Directory of open access journals, pp: 2104-2107,

VOL. 2182

LANDMARK-BASED CAR NAVIGATION WITH OVERTAKE CAPABILITY IN MULTI-AGENT ENVIRONMENTS

239