International Journal of Pure and Applied Mathematics
Volume 119 No. 18 2018, 515-523
ISSN: 1314-3395 (on-line version)
url: http://www.acadpubl.eu/hub/
Special Issue
http://www.acadpubl.eu/hub/
Review Finite Automata Application in Car Sensors
1
Mahmood Muayad Mohammed Al Khayyat
1
Ministry of Education, Iraq.
Abstract
In this paper we are reviewing the automata applications in car sensors
and we are talking specifically about the car sensors and how it assist the
driver and help him to drive safely and easily. The purpose of using
sensors in cars not only to assist the driver but to alert or control the car to
obviate accidents that may occur during driving.
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1. Introduction
Car sensors are very interested subject and in the same time is important to
know how can problems that related to this area of study are solved using finite
automata theory. The sensors uses wireless networks to communicate with the
control center or between cars in range of wireless coverage to share the
information by ad hoc connection. The shared information analysis by the car
system and will translate it to movement or control as example controlling the
steering wheel or accumulator or controlling the braking system. All of that will
assist the driver to detect a collision that may occur between two or more cars to
avoid it. The other advantage is to reduce the traffic in roads and manage car
safety [6]. Actually all of the above will lead to increase the safety and the
efficiency of travelling in vehicle and make the driving more easer and
comfortable.
2. Using Sensors
In recent years the usage of sensors is increased in all fields such as Health,
Computer science, Community, sports, security and vehicles..etc.the reson of
the wide usage of the sensors because it uses wireless technology.
We can divide the usage of the sensors in cars into three categories the first is
using GPS, the second is using sensors in environment (out the car) and the
third is built in car sensors
1. Global Positioning System (GPS)
GPS is helpful in road curvature estimatorfor real-time situation and As we sow
in [1] the GPS gives information to car system like the road curvature, car speed
and the direction in this case the car uses this information to control the steering
wheel
2. Environmental Sensors Placing
In this section we are reviewing the usage of the sensors in the surroundings
areas to give the cars all the information that is needed to assist the driver or
enhance the safety by providing coverage of wireless sensors networks (WSNs)
that communicate with the cars uses deferent systems like mobile sensor
networks (MSNs) or intelligent transportsystem (ITS) [2]. The sensors must be
placed in deferent areas in such a way to have reliablecoverage but there were
some problems faced with the covering areas in MSNs they were solve in
optimised random structure of vehicle sensor network (ORSVSN) technic use
cars information that sent from cars system to the control center. The
connection in the VSN uses ad hoc to authenticate the connection between cars
or the control center. The most important requirements for this system is large
number of car sensors to give the information to the control center. In another
study we noticed the usage of learning automataalgorithm step by step to
arrange the sensors over the environment and how it works efficiently [3].
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Using DFAs to Reduce the Number of Memory Access Problem
In environment sensors we have to determine the size of memory access and the
number of access per minute by all the cars sensors that in range and so on. In
this case we faced some problems but the most noticed one is the construction
of all possibilities states and the number of the transitions in it that denoted to
memory access. This problem was solved in Differential Encoding of DFAs for
Fast RegularExpression Matching [4] using δFA technic. The main purpose of
using δFA is to reduce the usage of memory because DFA uses large number of
bits because of the big number of the states. We cannot use NFA here because
of its parallel characteristics in this case we need more time to follow each input
transition that will lead us to more disadvantages there for DFA is better than
NFA thus to reduce the access timeto the control center. The control center
information came from deferent car sensors that are collected from deferent
places and the information messages transmitted by hop to hop delivery using
Hybrid Automata to reduce the access time to reach a reliable connection [5].
Using Regular Expression Matching on Compressed Traffic
This part of study will show the usage of the deep packet inspection in network
communication security and how it compressed traffic using regular expression
matching based on Deterministic Finite Automata (DFA)
3. Built in Car Sensors
Car sensors as we know are controlling the car system and each sensor is
responsible for calculating or collecting information such as car position or
sensing the surrounding objects..etc. We can list some of the sensors type by the
usage as braking control, accumulation control, seat belt and air bags control,
crash detection and vehicle position control, distance sensing parking control
and steering control.
Vehicle Position Estimation
According to [13]we need a distance before car crash at least 100-200 ms to
detect collision in such a case to worn the driver or to control the braking
system to avoid an inevitable collisionthis result achieved via Anisotropic
Magneto Resistive(AMR) sensors and position estimation algorithm using
finiteautomata machines as we see in fig.1 where In state 0, theestimator will
use the sonar sensor to update position since theAMR sensors are not yet
affected by the approaching vehicle.As soon as the AMR sensors respond to the
approaching vehicle, updates would be done using both sonar and AMRsensors
(state 1). When the vehicle enters a distance where thesonar readings are no
longer valid due to very small distances,updates would be done using only the
AMR sensors (state 2). Where cov is covariance andxth isthreshold distanceand
B is the magnetic field.
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Cov{B1(tk – a :tk)}<covth
State 0:
Sonar
updates
X(tk)<xAMR_th
State 1:
AMR update +
Sonar updates
X(tk)>xSonar_th
X(tk)<xAMR_th
Stat 2:
AMR
updates
Fig. 1: Architecture of the New Estimator Using Sensor Fusion
Formulate Car Society Using Cellular Automata
Cellular automata(CA)clustering using the zone of interest (ZOI) for mobicast
communications in vehicular ad hoc network (VANET) environments to
enhance the communications network between cars to obtain vehicle to vehicle
communication (V2V) [15]. The information are exchange between cars based
on ZOI as in Fig 2.
The achievements of this study is to reach CA simulation to group vehicles
according to velocity and to integrates CA clustering with the interest ontology
of users. Eventually inpeer-to-peer applications between passengers are
ofinterest for VANET networks. The networks allowing passengers in vehicles
to share music, movies, and other media, chat with each other, and play games.
The study case also reduces the overall traffic in highly mobile VANET
networks.
Planning for Car-parking Control
In car parking also uses finite automata is appeared for constricting architecture
of the model [17] this study focuses on two parking environments for
simplicity. One isa garage parking and the other is a parallel parking. So here
we have two variables The width of the road and the width of the parking lot as
shown in fig. 3. wehave four path types according to the control input to
represent the Rotation Motion (RM) as shown in fig. 4.
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Fig. 2: vehicular Ad Hoc Network (VANET) based Interest Ontology for Car Society
X
Y
Fig. 3: Parking Environment, the Width of the Road a and the Width of the Parking lot b
X: Garage parking.
Y: Parallel parking.
Fig. 4: Four Path Types According to the Control Input Type
Agent-based Using Automata Algorithms
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The applied automata algorithm receives the internal agent-based architectural
model of kinesinnanomotor as a deterministic finite automaton (DFA) model
and generates a regular machine language were Kinesin is a protein-based
natural nanomotor that transports molecular cargoes within cells by walking
alongmicrotubules (MT) [11].
According to Fig. 4, the definition of agent-based architectural DFA model of
kinesinnanomotor,
Mkinesin= (Q, Ʃ, δ, q0, F) was as follows:
1. Q = {q0: the nanomotor is disassociated from MT,
q1: the nanomotor is active at the starting point of movement,
q2: one head of the nanomotor is locked to MT and the other
head moving towards the plus-end of MT,
q3: ATP hydrolysis occurs,
q4: the nanomotor is inactive},
2. Ʃ= {a: cargo is available for the nanomotor,
b: MT is available for the nanomotor,
c: cargo and MT are available for the nanomotor
simultaneously,
d: the release of ADP and binding of ATP in the motor
domain,
e: the second head of the nanomotor interacts with MT,
f : the release of Pi,
g: the nanomotor transfers cargo to another nanomotor,
h: the nanomotor releases cargo at an appropriate site of the cell},
3. δ= { δ (q0, a) = q0, δ (q0, b) = q0, δ (q0, c) = q1,
δ (q1, d) = q2, δ (q2, e) = q3, δ (q3, f ) = q1,
δ (q1, g) = q4, δ (q2, g) = q4, δ (q3, g) = q4,
δ (q4, a) = q1, δ (q1, h) = q0, δ (q2, h) = q0,
δ (q3, h) = q0}
Fig. 5: Agent-based Architectural DFA model of kinesinnanomotor, Mkinesi
Sensors Control
In this part we will review the sensors control problems solving using
automaton approach. We can formulate the first problem as dynamicsensor
activation policy and the solution was using multi-agent systems, see [10], as in
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fig. 6 and fig. 7.
Fig. 6: The Sensor Activation Policy for Agent 1
Fig. 7: The Sensor Activation Policy for Agent 2
The second problem was how to fusion multi sensors data this problem was
solved by using logic gates (OR, NOR) in order to be able to model the different
frames of perceptionof several sensors outputs by difinean extended frameof
discernment, where each sensor can make a decisionsabout its own frame, see
[16].
Another study using automata investigates control design for the platoonof
automated vehicles whose sensors have limited sensing capability. A novel
hybrid platoon model is established, in whichactuator delay (e.g., the fueling
and braking delay) and the effectof sensing range limitation are involved [12].
Acknowledgment
This work is a part of our master study in Theoryofcomputation subject. We
would like to thank our lecturer Dr. ZuraidaAbalAbas for her support.
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