ROUTE PLANNING FOR THE BEST VALUE FUEL

Alva Sheehy and Kenneth Dawson-Howe

School of Computer Science and Statistics, Trinity College, Dublin 2, Ireland

Keywords: Route Planning, Optical Character Recognition, Petrol Price Determination, Vision Application.

Abstract: The system described in this paper is an extension to the standard satellite navigation systems used in

vehicles. A new route planning algorithm is developed using both shortest path and simplest path criteria

and then this algorithm is extended to allow the incorporation of a visit to a petrol station along the route.

The choice of petrol station is based on a combination of the relative location of the petrol stations and the

cost of petrol at those stations. The price of petrol at each petrol station is constantly updated on a central

server which is provided with prices by all vehicles which are using the system as they pass by. The price of

petrol is determined using a camera mounted on the dashboard of the car, the images from which are

processed with reasonably standard OCR software. When a vehicle requires fuel the central server provides

prices for petrol stations in the vicinity of the planned route.

1 INTRODUCTION

Satellite Navigations (SatNav) systems are in

everyday use, but are generally based on static route

maps. These systems can be extended by, for

example, using dynamic maps, incorporating

information about road-works, accidents and traffic

flow. This paper looks at a novel way of extending

SatNav systems by incorporating a facility to help

direct the vehicle to the best value fuel. There are

two major questions for such a system. First, how

does the central server get information on petrol

station location and prices? Second, what does the

SatNav do with this information? These questions

essentially divide the system into two main parts, the

acquisition of information and the use of this

information.

To acquire the current fuel prices we assume that

a large number of vehicles are subscribed to the

system and that these vehicles are all fitted with the

required navigation device which will incorporate a

dashboard camera looking forwards through the

windscreen. When the vehicles are being driven

around the system automatically reads (using image

processing) the petrol prices at each station passed

and transmits this price information (along with the

location of the petrol station) to the central server.

See Section 2 (Image Processing).

When a vehicle requires fuel a request is made to

the central server for the most up-to-date fuel prices.

The SatNav system can then determine which station

to incorporate into the route. This decision has to be

based on (1) fuel prices at the different stations, (2)

the additional financial cost of visiting each petrol

station in terms of distance, and (3) the additional

time and complexity cost of visiting each petrol

station. In fact it is necessary for the user to

explicitly decide upon the relative importance of

time/complexity cost vs. financial cost. See Section

3 (Route Planning).

2 IMAGE PROCESSING

In order to obtain the fuel prices images from the

dashboard mounted cameras must be processed to 1)

locate the petrol station signs (See Figure 1) and 2)

determine the price of the petrol. To locate the petrol

station sign we make use of Optical Character

Recognition (Cheriet 2007) by employing the

Tesseract OCR software (Smith2007) to locate the

keywords “Unleaded” and “Diesel”. If either word is

found we proceed to attempt to locate both the other

word and the prices.

The petrol prices are generally to the right of the

“Unleaded” and “Diesel” keywords and so we crop a

section of the image around the keywords located

(See Figure 2). Note that if one of the keywords was

not found in the initial search a larger region than

those shown in Figure 2 is cropped (i.e. including

173

Sheehy A. and Dawson-Howe K. (2010).

ROUTE PLANNING FOR THE BEST VALUE FUEL.

In Proceedings of the International Conference on Computer Vision Theory and Applications, pages 173-178

 SciTePress

Figure 1: Example images of petrol station signs.

the area above and below the keyword found) and

the keywords are searched for using Tesseract once

again. They frequently allows the other keyword to

be found as the area being processed does not

contain much background clutter (such as that

shown in Figure 1) and hence the processing will

often more reliably threshold and locate the words.

Figure 2: Cropped image based on keyword location.

Many (or perhaps most) petrol station signs use a

large number of LEDs to display the prices (e.g. See

Figure 2). This poses a problem for most OCR

packages as they cannot recognise the characters. To

overcome this a morphological closing operation

(i.e. a dilation followed by an erosion) is applied to

the binary images (See Figure 3). In fact it was

necessary to apply this process iteratively using

progressively later morphological filters until the

petrol price could be reliably determined. Note that

for this demonstrator system it was assumed that the

decimal place was always 0.9 and hence it was not

processed.

Figure 3: The result of closing operations on the binary

image shown in Figure 2.

The method was validated using a small sample set

of 8 images of different petrol station signs.

3 ROUTE PLANNING

This section deals with how the navigation system

chooses routes. Please note that what is presented

here is a summarised version of the route planning

which was developed. For further details including

more motivation behind the developed cost

functions see (Sheehy 2009).

3.1 Background

3.1.1 Simplest Route vs Shortest Route Cost

Functions

Shortest-path methods can take into account a

number of different criteria when computing an

“optimal” path (sequence of roads) between an

origin and a destination. The cost function

associated with any particular route can be weighted

to take into account such criteria as distance

travelled or time taken to travel a route. Penalties

assigned to particular roads can also be taken into

account. By applying weightings to these criteria,

combinations of route finding criterion can be used

in determining an optimal path between two points.

The criteria used with shortest-path methods are

usually “concrete” physical criteria such as the

physical length of a particular road or edge.

Often the driver would prefer to be given a

simple, easy path to follow in preference to a path

which is more complicated but is distance-wise,

shorter. This is especially true if the shortest path

would involve many turns and smaller roads etc. A

number of papers (Riesbeck 1980, Elliott 1982,

Streeter 1986) dealing with cognitive studies suggest

that the overall complexity of a route is just as

important in developing a good navigation system as

being able to find routes with shortest times or

distances. (Riesbeck 1980) emphasises the

importance of clear, concise directions while (Elliott

1982) studies the directions given by people when

asked how to get from a particular origin and

destination. (Streeter 1986) in particular discusses

the important factors which users consider in

choosing a route. The number of turns as well as the

number of traffic lights on a particular route was

given importance by the users, just as was the

“classical” idea of the time taken and the distance

travelled. This all suggests that successful route

planning systems must take all such criteria into

account.

(Mark 1986) conjectures that the simplest routes

to direct would also be the simplest for the driver to

navigate, thus minimising the chance of errors due to

VISAPP 2010 - International Conference on Computer Vision Theory and Applications

174

navigational problems. The algorithm put forward in

(Mark 1986) takes into account both the length of

the route and the complexity of its description. He

does this by classifying intersections by how

difficult they are to guide a user through and

adjusting the weights of each route accordingly.

An improvement suggested in (Duckham 2003),

would be to take into account the type of roads in the

network. Their algorithm relies purely on evaluating

the complexity of navigating a particular route. As

such there is no account made for how quickly a

particular road can be traversed, or indeed the speed

limit or the number of lanes on the road. It would

also be good to direct the algorithm to more

important roads. That said, the authors note that the

simplest route is often already directed to the more

important roads.

3.1.2 Search Algorithm

In order to find an optimal route from a given origin

to a given destination a search algorithm must be

used. While Dijkstra’s algorithm (Dijksra 1959) and

some of its modified versions (Fu 2004, Sanders

2007) are widely recognised as some of the best

route finding algorithms (as they are guaranteed to

give the optimal result), they are very labour

intensive both in terms of time and memory usage.

This is due to the fact that they blindly searches for

routes, looking in all directions equally.

To overcome these problems the A* or Goal-

directed search algorithm (Guzolek 1989, Ikeda

1994) can be used. This algorithm incorporates an

estimate of the remaining cost into the metric (i.e.

f(n) = g(n) + h(n) where g(n) is the actual cost from

the origin to node n and h(n) is an estimate of the

cost from node n to the destination. In order to guide

the search and in order to guarantee an optimal

solution, the value of h(n) must be a lower bound for

the remaining cost from the current node to the

destination. When using shortest path methods, the

value of h(n) is generally based on the Euclidean

distance and the maximum speed limit for the road

network as this would give the minimum time to get

to the destination. However, it is not as easy to use

the A* algorithm when simplest path criteria dictate

in the cost function. This is due to the fact there is no

way to adequately determine a lower bound for the

simplest path criteria from node n to the destination

as these criteria generally do not depend on the

relative position between node n and the destination.

The only lower bound which could be chosen which

would ensure a minimum bound would be zero

(which in most cases would correspond to node n

being on a road which itself leads to the destination

without any instructions needed along the way).

Having h(n) = 0 however, gives the Dijkstra

Algorithm itself and so there would be no speed-up

or increase in efficiency as the search would not be

guided in any way.

3.2 Combined Simplest Route &

Shortest Route Cost Function

We have developed a cost function which combines

both the simplest route and the shortest route costs.

To compute the shortest path we use the following

formulas.

c(R) = Σ

= Σ

(TravelTime

+ RoadDelay

+ TurnDelay

)

(1)

TravelTime = Distance / SpeedLimit

(2)

where Distance is the distance of the road,

SpeedLimit is the legal speed limit of the road,

RoadDelay is the time delay in traversing a

particular road (due mainly to traffic), and

TurnDelay is the time needed to complete a specific

turn. Note, the value for TurnDelay is the time delay

in travelling from the previous road (road i-1) to the

current road (road i).

To incorporate the simplest path with this metric

we compute:

COST(R) = ( Σ

(TravelTime

+ RoadDelay

+ TurnDelay

) * RoadTypeWeight

LaneNumberWeight

) * Π

TurnWeight

(3)

where RoadTypeWeight

is the road type weight

which is assigned to road i. Take the following

example: a minor road has a weighting of 1.1, a

major road (of the same length) a weighting of 1

Each road has an initial cost function (total time of

travel) of 4. When taking road type weighting into

account the cost value ci for the minor road is now

4.4 while the cost value ci for the major road is now

4. Because the major road results in a lower cost

function, an algorithm which favours lower cost

functions will thus favour routes with major roads.

LaneNumberWeight

is a similar weighting factor

which gives more cost to roads with single lanes (as

more major roads facilitate easier overtaking of slow

vehicles and are generally better signposted). The

TurnWeight takes into account the complexity

associated with navigating a turn. For example a

right turn off a busy road without the aid of traffic

lights is a much more stressful and complicated turn

to take for the driver than a simple slip road off to

the left.

ROUTE PLANNING FOR THE BEST VALUE FUEL

175

Figure 5: A road network with a car en route to a destination on which are shown three possible routes via different petrol

stations. See text for explanation of route selection.

EuroCost is the cost associated with detouring to a

given petrol station and w is the weighting factor

which is used to weight between the two different

cost functions. A weighting factor of w=1 would

result in choosing a petrol station which would give

the best route, regardless of the monetary cost. A

weighting factor of w=0 on the other hand would

choose a petrol station based on its monetary cost

alone, regardless of the route to get there.

(Remember that even though w=0 results in no

comparison of the routes to different petrol stations,

the route to get to these petrol stations are still

chosen by the normal route finding algorithm and so

are still the optimal routes to get to these particular

petrol stations.)

In examining a particular petrol station, in order

to improve on efficiency and reduce the number of

petrol stations examined, the total overall cost is

calculated at each iteration of the route finding

algorithm. This is to cut off the search for a route to

that petrol station (and hence ignore that petrol

station) if the overall total cost is greater than the

minimum cost found so far.

However, in order to compute the euro savings a

value for the distance of the overall route needs to be

found. As the euro savings value is being computed

during the route search, this is clearly not possible.

An estimate is used however, in a similar idea to that

of the h(n) function in the A* algorithm. A lower

bounds value for distance can be computed using

Euclidean distances. As such, if a route search has

not reached the petrol stations yet the value for the

distance of the route dist

Route

will be;

dist

Route

= dist

RouteSoFar

+ est

n,station

+ est

station,dest

(8)

where dist

RouteSoFar

is the distance of the route

from the origin to the current position n, est

n,station

the Euclidean distance from the current position n to

the petrol station and est

station,dest

is the Euclidean

distance from the petrol station to the destination.

4 VALIDATION

The image processing developed in section 2 was

tested on a limited number of test images. The route

planning developed in the previous section has been

tested in a simulated environment. Initially scenarios

were developed to test that the route planning

algorithm worked appropriately in a variety of

situations.

To validate the petrol price metrics a few

different scenarios were considered. In Figure 5

below a car is shown on a road network with a

destination shown by the red star. It is assumed that

petrol is required and three routes which include

petrol stations with associated prices are considered

(A is €1.02, B is €1.00 and C is €1.04). When the

weighting factor, w = 0.3 petrol station B (Route 1)

is chosen. This route is also chosen for w=0 which

implies that this is the route with the best monetary

savings. When w = 0.7 petrol station A (Route 2) is

ROUTE PLANNING FOR THE BEST VALUE FUEL

177

chosen. Finally when w = 0.95 petrol station C is

chosen. This is also true for w = 1 so this choice of

petrol station gives the best overall route.

The scenario of changing petrol prices has also

been considered and examples shown where the

selected petrol station (and route) will change as the

prices vary.

5 CONCLUSIONS

A prototype system has been developed

demonstrating all of the technology required for the

application proposed. More importantly the

mathematics concerned with route planning and with

integrating the choice of petrol station based on

price have been developed and a basic validation

achieved.

To further this work we need to integrate a real

satellite navigation system with a camera system and

a mobile communications system. Practically, this

will require OEM involvement.

REFERENCES

Cheriet, M., Kharma, N., Liu, C., Suen, C., 2007.

Character Recognition Systems: A Guide for Students

and Practioners, Wiley-Interscience.

Dijksra, E., 1959. A Note on Two Problems in Connexion

with Graphs. In Numerische Mathematik, Pages: 269 –

271.

Duckham, M., Kulik, L., 2003. “Simplest” Paths -

Automated Route Selection for Navigation, In Lecture

Notes in Computer Science: Spatial Information

Theory, October, Vol. 2825, Pages: 169 - 185.

Elliott M., Lesk, M., 1982. Route Finding in Street Maps

by Computers and People, In Proceedings of AAAI-82

Conferences 1982.

Fu, M., Li, J., Deng, Z., 2004. A Practical Route Planning

Algorithm for Vehicle Navigation System, in Fifth

World Congress on Intelligent Control and

Automation (WCICA). Vol. 6, Pages: 5326 - 5329.

Guzolek, J., Koch, E., 1989. Real-time Route Planning in

Road Networks. in Vehicle Navigation and

Information Systems Conference, Sep 1989, Pages

165-169.

Ikeda, T., Hsu, M. Imai, H., Nishimura, S., Shimoura, H.,

Hashimoto, T., Tenmoku, K., Mitoh, K., 1994. A Fast

Algorithm for Finding Better Routes by AI Search

Techniques”, In Proceedings of Vehicle Navigation

and Information Systems Conference, 1994, Pages:

291 - 296.

Mark, D., 1986. Automated Route Selection for

Navigation. in IEEE Aerospace and Electronic

Systems Magazine 1 1986, 2-5.

Riesbeck, C., 1980. You Can’t Miss It’: Judging the

Clarity of Direction. In Cognitive Science 4, Pages:

285 – 303.

Sanders, P., Schultes, D., 2007 Engineering Fast Route

Planning Algorithms. In Lecture Notes in Computer

Science: Experimental Algorithms, June 2007, Vol.

4525, Pages: 23 - 36.

Sheehy, A., 2009. A Driver’s Assistant: Finding the Best

Routes and Petrol Stations, MSc thesis, Dept. of

Computer Science, Trinity College, Dublin 2. Ireland.

Smith, R., 2007. An Overview of the Tesseract OCR

Engine. In Ninth International Conference on

Document Analysis and Recognition, Vol. 2, Pages:

629 – 633.

Streeter, L., Vitello, D., 1986. A Profile of Drivers’ Map-

Reading Abilities, In Human Facts, 28, Pages: 223-

239

VISAPP 2010 - International Conference on Computer Vision Theory and Applications

178