chapter 5 longitudinal control organization

114
CHAPTER 5
LONGITUDINAL CONTROL ORGANIZATION
Sensors used for track sensing, track sensing methods and steering
control methods based on sensing strategies are discussed in chapters 2, 3 and
4. The algorithms for adaptive speed control of the vehicle, obstacle detection
and collision avoidance, vehicle communication used for intersection
collision avoidance and vehicle platooning concepts are discussed in this
chapter which forms the longitudinal control of the vehicle.
5.1
INTRODUCTION
Driving in traffic jam conditions is one of the most challenging
topics of large city traffic management. The data on Madrid (Spain) indicate
that almost one million workers every day waste more than 30 minutes at rush
hours because of traffic jams. This problem is being tackled by both the
automotive industry and transport research groups with the goal of reducing
these figures. With respect to the automotive sector, particular effort has been
put into developing automatic vehicle speed control. The longitudinal control
of a vehicle includes various aspects regarding the speed of the vehicle. It
covers adaptive speed control, adaptive cruise control, vehicle communication
and platooning. Due to various inescapable reasons the speed of the vehicle
might get changed, than the expected speed(Lin et al 2010). Some major
instance that changes the speed of the vehicle is the effect of changes in the
power supplied by the battery, the friction produced between the wheels and
the track as the vehicle takes a turn, slopes in road, load variations, etc.
115
Presence of turns in the track is unavoidable and hence a suitable algorithm
needs to be developed and employed to overcome this impediment. Thus
adaptive speed control becomes mandatory in automated self guided vehicle.
Different speed control and optimization techniques are implemented and
their performances are analyzed in this chapter.
5.2
LITERATURE SURVEY
The main aim of these longitudinal controllers is to improve the
safety of the occupants by relieving the human driver of tedious tasks so as to
make driving easier, as well as making traffic flow more efficient. A first
implementation was cruise control (CC) based on controlling the accelerator
pedal (Aono and Kowatari 2006). This was then extended to Adaptive Cruise
Control (ACC) systems (Moon et al 2009), developed to maintain certain
speeds. A study of the impact of the wide spread inclusion of CC systems
shows that there was a 50% reduction in crashes with injuries to the vehicle’s
occupants.
For urban environments, several advanced driver assistance
systems (ADAS) (Lindgren et al 2009), have been developed based on
acoustics and visual signals to warn the driver of potential collisions, but the
trend for the future is to develop automatic driving controls instead of
developing driving aids (Milanes et al 2011). With these premises, research
on autonomous systems capable of adapting a vehicle’s speed in urban
environments is one of the most important targets for the mid-term future of
the market. Such systems, based on combined actions on the accelerator and
brake pedals, are known as intelligent cruise control (ICC) (Girad et al 2005).
Recent approach towards this problem was studied in scaled vehicles (Cai et
al 2010), but gasoline-propelled vehicle dynamics at very low speeds are
highly non-linear and difficult to translate from a scaled vehicle to the real
world. Since the essential mechanism to generate friction or braking efforts is
116
the tire road interaction, which is a very complex phenomenon and depends
on many poorly, known factors, the control strategies chosen for this work
have a common point and they are all based on model free control approaches.
The recent explosion of electric vehicle capabilities, including
batteries, plug-in-hybrids, fuel cells, and in-wheel/hub motor technologies,
has fostered a significantly increased pace toward electric ground vehicles
(EGVs) with independently actuated axial and in-wheel/hub motors. In-wheel
motor technology has become mature and allows fast and accurate torque
control for each of the EGV wheels (Hallowell and Ray 2003). Electric
concept cars with independently actuated in-wheel motors, such as GM Geo
Storm, Mitsubishi iMIEV, and Volvo Recharge, have already proceeded into
the prototyping. Compared with the conventional vehicle drive train
architectures where the driving and braking actions of different wheels are
coupled, EGVs with independently actuated axial or in-wheel motors can
offer higher control flexibility and many other potential advantages. The
proposed work uses the Electric Ground Vehicle (EGV) and uses different
algorithms for longitudinal control which includes adaptive speed control
algorithm, adaptive acceleration control algorithm and obstacle detection and
collision avoidance algorithm. These algorithms are simulated and the
performances of these algorithms are tested in the prototype vehicles in real
time.
5.2.1
Vehicle Communication
Vehicle to Vehicle communication is a popular area of research
because of the numerous application possibilities for driver safety and
comfort. More specifically Vehicular Ad-Hoc Networks (VANET’s) are of
interest because they have the potential for low latencies, and allow network
connection between vehicles to stay active even when they are moving at high
speeds.
117
The development of vehicle communication systems and related
technologies has been the subject of numerous projects around the globe as
well as for standardization working groups. The summary of the large
majority
of
such
recent
efforts
is
shown
in
Table
5.1.
From
Table 5.1, it is understood that all the projects having complementary but
often similar objectives and approaches. This multitude of concerted efforts
and approaches indicates the need for coordination between vehicles.
Table 5.1 Vehicle Communication Projects Worldwide
Project
name
Project Information
Period
2006
AKTIV
to
2010
External
funding
Brief description of objectives
Design, development, and evaluation of
Ministry of driver assistance systems, knowledge
Economics and and information technologies, efficient
Technology traffic management, and V2V and V2I
communication;
Germany
http://www.aktiv-online.org/index.html
Car to Car
Communication
On
Consort- going
ium (C2CCC)
N/A
Development of a European industry
standard for VC communication
systems, active safety applications
prototyping and demonstrations,
harmonization of VC standards
worldwide, realistic deployment
strategies and business models;
http://www.car-2-car.org/
COM2
REACT
Distributed Traffic Application, based
2007
on cellular and V2V communication, into
European Union car and V2V communication systems,
vehicle to center communication:
2008
http: //www.com2react-project.org/
118
Table 5.1 (Continued)
Project
name
Project Information
Period
External
funding
Brief description of objectives
Telematic applications for the road
infrastructure, cooperative traffic
2006 to
Coopers
European Union management involving vehicles and
2010
roadside infrastructure;
www.coopers-ip.eu/
Cyber
Cars 2
ETSI TC
ITS
Cooperation between vehicles running
at close range (platooning) and at
2006 to
European Union
intersections (merging, crossing);
2008
http:cybercars.org/
On
going
N/A
Standardization activities to support the
development and implementation of
intelligent transportation systems;
http://portal.etsi.org/Portal_Common/ho
me.asp
Automated metering, queue assistance,
temporary auto-pilot, and active green
2008 to
HAVE-IT
European Union driving mechanisms, integrated in six
2011
demonstrator vehicles;
http://www.haveit-eu.org
Smart
Way
Ministry of
Driving safety support systems based on
Land,
2006 to
Infrastructure, vehicle-highway cooperation.
2010
Transport and http://www.mlit.go.jp/road/ITS/
Tourism, Japan
PATH
Multidisciplinary research program
administrated by the UC Berkeley
California
Institute of Transportation Studies and
Department of
CalTrans; activities in four areas; policy
Transportation
and behavioral, transportation safety and
(CalTrans)
traffic and transit operations research.
http://www.path.berkeley.edu/
On
going
119
Wireless transmission and medium access technologies adapted to
the vehicle communication environment are the primary enabling technology.
Conceptually, on top of them, networking technologies allow for data
exchange among nearby and remote devices (vehicles, roadside infrastructure
units and other servers). The vehicle communication computing platform is
functionally independent and responsible for running the vehicle-to-vehicle
(V2V) and vehicle-to-infrastructure (V2I) communication protocols and the
supported applications.
The vehicle-vehicle communication capability is used to coordinate
manoeuvring. These manoeuvres include lane changing, in which a vehicle
safely coordinates its lane change with adjacent vehicles, so that they do not
try to occupy the same place at the same time, and platoon join and split
manoeuvres, decreasing the space between vehicles to form a platoon and
increasing the space to separate from a platoon.
An important component of Intelligent Vehicle Highway System
and Automated Highway Systems are the intelligent vehicles (IVs), which
sense the environment around them using sensors (such as radar, lidar or
machine vision techniques) and strive to achieve more efficient vehicle
operation either by assisting the driver or by taking complete control of the
vehicle (Bishop 2000).
These IVs also support vehicle to vehicle and vehicle to roadside
communication. Based on the extent to which the roadside and vehicle could
work together (without human driver intervention), we can discern different
types of Automated Highway Systems as follows
120
5.2.1.1
Autonomous Vehicle Systems
Vehicles are equipped with sensors and computers to operate
without roadside infrastructure assistance and without coordination with
neighbouring vehicles.
5.2.1.2
Cooperative Vehicle Systems
Vehicles use sensors and wireless communication techniques to
coordinate their manoeuvres with neighbouring vehicles without any roadside
intervention.
5.2.1.3
Infrastructure Supported Systems
Vehicles communicate with each other and guidelines for decision
making purposes are provided by the roadside infrastructure.
5.2.1.4
Infrastructure Managed Systems
Vehicles indicate their desired actions such as lane changes, exits
and entries to the roadside infrastructure. The roadside system then provides
the instructions for inter vehicle coordination of these manoeuvres.
5.2.1.5
Infrastructure Controlled Systems
The roadside infrastructure takes entire control of the vehicle
operations, monitors the traffic, and optimizes the vehicle operations in such a
way that, the network is utilized as well as possible (Baskar et al 2011).
The proposed work implements autonomous vehicle systems, cooperative vehicle systems and infrastructure controlled systems in the
prototype vehicles and its performances in the test bed environment are
analyzed in real time.
121
5.2.2
Vehicle Platooning
The concept of fully automated vehicles travelling in electronically
coupled platoons is not new one (Caudill and Garrard 1977). The platoon
demonstration was designed by researchers at the California PATH program
to show how vehicle automation technology can be used to make a major
contribution to relieving traffic congestion. The eight vehicles operating in
tight coordination showed how an automated highway system can provide a
significant increase in highway throughput (vehicles per lane per hour moving
along the highway).
Since platooning enables vehicles to operate much closer together
than is possible under manual driving conditions, each lane can carry at least
twice, as much traffic as it can today. This should make it possible to greatly
reduce highway congestion. Also, at close spacing aerodynamic drag is
significantly reduced which can lead to major reductions in fuel consumption
and exhaust emissions. The high performance vehicle control system also
increases the safety of highway travel, reduces driving stress and tedium, and
provides a very smooth ride.
Eight vehicles of the PATH platoon travelled at a fixed separation
distance of 6.5 meters which is shown in Figure 5.1. At this spacing, eightvehicle platoons separated by a safe inter-platoon gap of 60 m and travelling
at 65 mph would represent a “pipeline” capacity of about 5700 vehicles per
hour. Reducing this by 25% to allow for the manoeuvring needed at entry and
exit points corresponds to an effective throughput of about 4300 vehicles per
lane per hour. Throughput under normal manual driving conditions at this
speed would be approximately 2000 vehicles per lane per hour.
122
Figure 5.1 Eight Vehicles in Platoon
Such short spacing between vehicles can produce a significant
reduction in aerodynamic drag for all of the vehicles (leader as well as
followers). These drag reductions are moderate at the 6.5 meter spacing of the
Demo, but become more dramatic at spacing of half that length. Wind-tunnel
tests at the University of Southern California have shown that the drag force
can be cut in half when vehicles operate at a separation of about half a vehicle
length. Analyses at UC Riverside have shown how that drag reduction
translates into improvements of 20 to 25% in fuel economy and emissions
reductions.
The tight coordination of vehicle manoeuvring is achieved by
combining range information from forward looking radar with information
from a radio communication system that provides vehicle speed and
acceleration updates 50 times per second. This means that the vehicles can
respond to changes in the motions of the vehicles ahead of them much more
quickly than human drivers. As a result, the space between the vehicles is so
123
close to constant that variations are imperceptible to the driver and
passengers, producing the illusion of a mechanical coupling between the
vehicles. The proposed work uses four vehicles for platooning which is
discussed at the end of this chapter.
5.3
PROPOSED LONGITUDINAL CONTROL ALGORITHMS
In typical driving scenarios, when the human driver controls the
vehicle in a predefined lane, the driver performs four main tasks. The first is
to maintain an optimal speed as much as possible when no vehicle is in sight.
The second task is to keep a safe distance from any vehicle ahead. The third is
to deal with the situation when a sudden vehicle cuts in the driving lane. The
last is to react immediately as a response to an emergency which usually
implies a hard brake. The longitudinal controller designed should be able to
perform all these four tasks. The longitudinal control of a vehicle includes
three different algorithms which are proposed as shown in Figure 5.2, when
combined enables the vehicle to manoeuvre the track in optimal speed. The
first of the three calculates the critical speed with which the vehicle can travel
for the given turn radius of the track which is obtained by the steer value of
the vehicle and thereby setting the base speed of the vehicle. It tries to
maintain the actual speed of the vehicle as close to base speed of the vehicle
irrespective of the external conditions which is achieved by providing
necessary speed boosts and brakes whenever required. Second algorithm
focuses on altering the speed depending on the confidence obtained. (i.e.)
when lateral control gets more and more perfect, the speed increases
accordingly, and vice versa. Presence of an obstacle overrides the above two
algorithm and brings the vehicle to a stop beforehand at safe distance by the
third algorithm. These three algorithms running simultaneously in the vehicle
enables to achieve the optimal speed control. Steer error based speed control
124
algorithm and critical speed estimation algorithm provides the safety and
improves the performance to the vehicle in motion.
Obstacle
detection and
collision
avoidance
algorithm
Adaptive
acceleration
control
algorithm
Adaptive speed
control
algorithm
Longitudinal
Control
Figure 5.2 Proposed Longitudinal Control Algorithms
5.3.1
Adaptive Speed Control with Encoders
In order to make the vehicle to track the entire lap at desired speed
irrespective of the curvature and terrain of the track, adaptive speed control
algorithm is proposed. The speed acquisition module available in prototype
vehicle-1 is an encoder as discussed in chapter 2.5.2.1, which is an electromechanical device that converts the angular position of a shaft or axle to a
digital code, making it an angle transducer. The speed is dynamically
controlled once in 0.5 seconds. The number of pulses in this interval is predetermined and regularly compared with the instantaneous count and
appropriate speed control is performed using PWM. PACN10 register in S12x
microcontroller gives the number of rotations. The maximum number of
rotations per second is 10. The wheel circumference of the prototype vehicle
is 16cm so the maximum speed of the
160 cm/s.
prototype vehicle-1 is calculated as
125
The algorithm obtains feedback from the encoder corresponding to
the vehicle speed and is compared with the speed desired. When the speed of
the vehicle varies, the algorithm alters the PWM applied to the motor so as to
maintain the speed in the desired level. Constant rotation value (CR) of the
wheel is fixed for the constant speed of the vehicle. Current speed of the
vehicle (ROT) is measured and compared periodically with CR. Based on the
comparison result if the current speed is greater than the desired speed (i.e)
ROT > CR the vehicle speed is slowed down by reducing the PWM. If the
speed of the vehicle decreases from the desired speed (i.e) ROT < CR then
speed factor (Ks) is incremented for every iteration which increases the speed
by increasing the PWM duty gradually according to equation 5.1 until the
vehicle reaches the desired speed. Equation 5.1 shows how the PWM value
for the DC drive is assigned dynamically and they are given in Table 5.1.
PWM_Duty = Constant Duty + (Ks x (CR – ROT))
(5.1)
Table 5.2 Adaptive Speed Table
KS
PWM Duty cycle value
1
2
3
4
5
6
7
8
0
65
65
65
65
65
65
65
65
1
66
67
68
69
70
71
72
73
2
67
69
71
73
75
77
79
81
3
68
71
74
77
80
83
86
89
4
69
73
77
81
85
89
93
97
5
70
75
80
85
90
95
100
105
6
71
77
83
89
95
101
107
113
7
72
79
86
93
100
107
114
121
(CR-ROT)
126
In Table 5.2 the first row values are speed factor (Ks) which
increments every iteration. The first column value is the difference of constant
rotation value (CR) with the current speed (ROT). When the difference is
zero, the vehicle moves in desired speed hence the row values are 65 which is
the PWM duty value for the DC motor. When the difference is more, the
PWM duty value is increased every iteration to attain the desired speed.
Different possible PWM duty values with respect to the speed factor and
speed difference is depicted in Table 5.2. Thus the speed of the vehicle is
dynamically varied to maintain the desired speed independent of the road
conditions. This is the adaptive speed control method which is proposed. The
entire flow is shown in Figure 5.3.
Set constant speed
and make Ks=0
Get rotation (ROT)
of the wheel
Y
ROT>=CR
Make Ks=0 to set
constant speed
N
Increment Ks at
constant period
Increase the speed
Speed=Speed+(Ks*(CR-ROT)
Figure 5.3 Adaptive Speed Control Algorithm
127
Figure 5.4 shows the relationship between three parameters CRROT, Ks and PWM Duty. From the Figure 5.4 it is clear that when the speed
of the vehicle decreases, the vehicle will not move in the desired speed even
though the desired PWM duty value is given. Now the difference (CR-ROT)
will increase, which makes ‘Ks’ parameter to increment every iteration, which
increases the PWM duty gradually until the vehicle reaches the desired speed.
This process is clearly shown in Figure 5.3. From the Figure 5.4 if the CRROT value is seven and the Ks value is seven the PWM duty value is
maximum up to 140. Similarly, CR-ROT =0 and then the PWM duty value is
minimum of 65 as shown in the Table 5.1. The entire range of speed duty
value with respect to ‘CR-ROT’ and ‘Ks’ value is plotted in Figure 5.4. This
method of speed adaptation makes the vehicle to maintain the constant speed
independent of road conditions and load conditions.
Figure 5.4 PWM_DUTY Vs CR-ROT Vs Ks
128
5.3.2
Adaptive Speed Control with Feedback Current
In this technique, the information about the speed of the vehicle
during the run is obtained by the current feedback from the DC motors. The
current drawn by the DC motor for various speed levels are as shown in
Figure 5.5. Table 5.3 and 5.4 shows the DC motor PWM value under no load
condition and full load condition for the differential drive DC motors. The
present speed of the vehicle is calculated from the feedback current value and
compared with the base speed. If the present speed is slower than the base
speed PWM is increased iteratively to get the desired speed and vice versa.
Current Feedback
Current (adc value)
70
60
50
LEFT
MOTOR
40
30
20
RIGHT
MOTOR
10
0
50
100
150
200
250
Speed duty value
300
Figure 5.5 Feedback Current from DC Motor
The speed of the motors vary involuntarily while making curves
due to various reasons such as friction, centripetal force etc. and to
compensate the above effect and to make the vehicle be in constant speed
during turns, a boosting algorithm is employed based on the current feedback
as shown in the Table 5.5.
129
Table 5.3 Feedback from DC Motors under No-Load Condition
Left Motor
(no load)
Right Motor
(no load)
DC Motor
PWM
Duty Value
Current
feedback ADC
Output (hex)
DC Motor
PWM Duty
Value
Current
feedback ADC
Output (hex)
50
05
50
06
100
0D
100
0E
150
18
150
17
200
24
200
25
250
2D
250
2E
300
38
300
3A
The Table 5.5 shows how the DC motor’s PWM is varied in order
to maintain the constant speed. In the table ‘BS’ denotes the base speed and
‘TS’ the true speed of the vehicle. The ‘Boost’ is computed as the difference
between the base speed and the true speed depending upon the boost value
the duty cycle is increased for every iterations in order to compensate the
reduction in speed as in Table 5.4.
Boost = BS –TS
(5.2)
New Speed = BS + (Boost * No. of iterations)
(5.3)
Table 5.4 Feedback from DC Motors under No-Load and Full-Load
Current feedback values (from ADC (hex) )
No Load Condition Full Load Condition
Left Motor Only
38
9B
Right Motor Only
3A
A4
(39, 3C)
(9D, A0)
Dual Drive (left, right)
130
The result of employing the boost algorithm rectifies the problem
of speed reduction. The performance of the algorithm is shown in the
Figure 5.6. From the Figure 5.6 it is observed that, for every iteration, the
PWM duty value increases based on the boost value.
Table 5.5 Adaptive Speed Control using Feedback Current
Iteration
1
2
3
4
5
6
0
BS
BS
BS
BS
BS
BS
1
BS+5
BS+10
BS+15
BS+20
BS+25
BS+30
2
BS+10
BS+20
BS+30
BS+40
BS+50
BS+60
3
BS+15
BS+30
BS+45
BS+60
BS+75
BS+90
4
BS+20
BS+40
BS+60
BS+80
BS+100
BS+120
5
BS+25
BS+50
BS+75
BS+100
BS+125
BS+150
Boost
350
PWM Duty Value
330
310
290
BOOST = 0
270
BOOST = 1
250
BOOST = 2
230
210
BOOST = 3
190
BOOST = 4
170
BOOST = 5
150
0
1
2
3
4
5
Iterations
Figure 5.6 Speed Boost Performance
131
When the boost value is minimum, the PWM duty value is
increased slowly for every iteration, but when the boost value is more the
PWM value is increased gradually with more speed to meet the required
speed condition. Because of the linear speed improvement we get better
vehicle stability as well as smooth speed variations independent of the road
condition as well as independent of the load condition. Thus the proposed
adaptive speed control algorithm based on the current feedback works
recursively and maintains the vehicle speed optimum over the entire lap of the
proposed track.
5.3.3
Adaptive Acceleration Control Algorithm
For achieving efficient performance from the vehicle, confidence
level factor is introduced. The confidence level is a factor which is mimicked
from human driving behaviour. When a particular task is performed without
error for a specific duration of time the confidence level increases and on
encountering errors often, the confidence level decreases. Similarly, the
acceleration of the vehicle is varied. The algorithm is similar to adaptive delta
algorithm where the delta change is made adaptive to the input signal. When
the correlation between the previous steer values and the present steer value is
high and also the error is minimum, then the vehicle is accelerated so as to
optimize the speed. Conversely when the correlation among the previous and
the present error values is low or the deviation from the track is high, then the
vehicle is decelerated to optimize the control. Thus both speed and steer
control of the vehicle is optimized by the use of adaptive acceleration control
algorithm. The graph plotted between the acceleration and the error values is
shown in the Figure 5.7.
132
Figure 5.7 Adaptive Acceleration Control Plot
When the deviation is less the acceleration is more and when the
deviation is more the acceleration is less. This technique improves the
performance by increasing the speed to a maximum in straight path and
maintains the optimum speed in the curves. This feature enables the vehicle to
complete the desired path in short duration of time.
The flow chart in Figure 5.8 clearly depicts the working of the
obstacle detection and collision avoidance algorithm. The foresaid condition
works fine until the distance between test vehicle and the preceding vehicle or
obstacle (O) is greater than 30cm. When the distance is less than 30cm and
greater than 20cm the speed is reduced by reducing the PWM duty value by
30%; When the distance is less than 20cm and greater than 10cm the speed
is reduced by reducing the PWM value by 60% else reduce the speed up to
90 %.
133
Set Base Speed
Speed Sensing
Find True Speed
N
O
>
30
N
O
>
20
Y
Y
N
O
>
10
30% Decrease
Duty Cycle
90% Decrease
Duty Cycle
Y
60% Decrease
Duty Cycle
Y
TS
>
BS
Decrease Duty
Cycle
Set Base Speed
N
Increase Duty Cycle
Figure 5.8 Obstacle Detection and Collision Avoidance Algorithm
When the distance between the vehicle and obstacle increases the
obstacle sensor value decreases and the speed is increased by increasing the
duty cycle value as shown in Figure 5.9.
134
Longitudinal Control
measured value
150
Distance
100
Obstacle Sensor
Values
50
Duty Value
0
1
3
5
7
9
Distance
11 13 15
17
Sample Points
Figure 5.9 Speed Adaptation Based on Obstacle
From Figure 5.9, it is clear that the speed is directly proportional to
distance of the obstacle. This speed control algorithm works based on the
nature of the obstacle and prevents the collision and hence it helps in collision
avoidance.
5.3.4
Steer Error based Speed Control
In steer error based speed control, the speed control is applied to the
vehicle without obtaining any feedback from the rear wheels at slow speed.
This technique is based on the fact that the speed of the vehicle reduces
during turns due to more frictional force and the reduction in speed is
proportional to the radius of curvature of the turn. The steer based speed
control method exploits this fact and the speed of the vehicle is varied in
accordance to the steer angle of the vehicle. Table 5.6 shows the variation of
speed with respect to the steer angle. From the Table 5.6, it is observed that
speed PWM is directly proportional to the steer error. When the error is more
the steer control algorithms steers the vehicle towards the centre of the track
which increases the friction and reduces the speed. To compensate, the speed
135
is gradually increased when the error increased but only up to the error value
which reaches 36.
Table 5.6 Steer Error Based Speed Control
Steer Error
PWM Duty
Cycle Value
1 to 8
50
9 to 15
52
16 to 22
54
23 to 28
56
29 to 36
58
>36
55
When error increases beyond 36 the vehicle goes out of track and
the speed is reduced to prevent the vehicle moving away from the track. Now
the retracing algorithm identifies the vehicle position whether the vehicle is in
either right/left side of the track by referring the previous sensor values and
steer the vehicle in the opposite direction to bring back the vehicle to the
trajectory.
5.4
CRITICAL SPEED ESTIMATION ALGORITHM
Speed of the vehicle during the turn in inversely proportional to the
sharpness of the curve. Thus the speed limit identification is the critical
requirement of the cruise control of the vehicle which assures the stability of
the vehicle during steering. The vehicle cannot turn a sharp curve with any
arbitrary speed. For safe cruising and skid avoidance, the maximum speed
threshold with respect to the turn radius is to be estimated.
136
From the basic laws of physics, the centripetal force is given by,
=
(5.4)
The frictional force acting on the vehicle is given as,
=
(5.5)
From the equations we obtain the result to be
=
(5.6)
where ‘ ’ is the static coefficient of friction, ‘g’ is acceleration due to gravity
9.81m/s2 and ‘r’ is the radius of the turn. The static coefficient of friction for
KT board / flux surface is obtained to be equal to 0.577. The relationship
between the critical speed and the radius of the curve are plotted and is shown
Critical Speed (m/s)
in Figure 5.10.
4
3.5
3
2.5
2
1.5
1
Critical
Speed…
0.5
0
0.2 0.4 0.6 0.8
1
1.2 1.4 1.6 1.8
2
Turn Radius (m)
Figure 5.10 Critical Speed Vs Turn Radius
137
5.5
VEHICLE COMMUNICATION AND VEHICLE
PLATOONING
Vehicles are equipped with novel computing communication and
sensing capabilities and user interfaces. It
will support a spectrum of
applications that enhance transportation safety and efficiency, but also
provide new or integrate existing services for drivers and passengers. A key
aspect of vehicle communication system is to expand the time horizon of
information relevant to driving safety and transportation efficiency, introduce
new information sources and improve its quality. The basis is a collaborative
approach, with each vehicle and roadside infrastructure units contributing
relevant information. Based on their own sensing and on information received
from nearby peers and roadside infrastructure units vehicles can anticipate,
detect, and avoid dangerous or unwanted situations. For example, timely
notifications about lane changes, emergency braking, and unsafely
approaching vehicles can be highly beneficial. The same is true for
notifications about dangerous or heavy traffic conditions predicted by
roadside infrastructure units locally or within a larger region with the help of
other vehicles.
Figure 5.11 Prototype-1 with Wireless Sensor Module and Obstacle Sensor
138
Figure 5.11 shows the prototype vehicle-1 with wireless sensor
module for wireless communication. The specifications of these wireless
sensor modules are discussed in chapter 2.4.4. Wireless sensor modules used
in the prototype vehicles communicate among themselves and also with
roadside infrastructure units which forms a collaborative communication
system. In the proposed work cooperative vehicle system and infrastructure
controlled system is used and its performance is analyzed in real time with the
small scale vehicles.
5.5.1
Cooperative Vehicle System
The task of entering and leaving the critical zone is taken as a
metric for this cooperative vehicle systems analysis. If a vehicle enters a
critical zone in the track, it broadcasts the information to the nearby vehicles
to make all nearby vehicles to stop until the vehicle comes out from the
critical zone. Once the vehicle comes out from the critical zone it broadcasts
another message to the neighbouring vehicles to start manoeuvring. In the test
bed track, the critical zone is the circle as shown in Figure 5.12. Whenever
any vehicle enters the critical zone that vehicle broadcasts the information to
other vehicles to stop in front of the critical zone until the vehicles exits the
critical zone. When the vehicle exits the critical zone it broadcasts other
message to make the next vehicle to enter into the critical zone. The vehicle in
the critical zone and the remaining two vehicles which are waiting near the
critical zone is shown in Figure 5.12. Thus vehicles communicate among
themselves whenever any vehicle enters/exits the critical zone thus preventing
intersection
collision
of
the
vehicles.
This
type
of
communication improves the safety to the autonomous vehicles.
collaborative
139
Figure 5.12 Cooperative Vehicle System
5.5.2
Road Side Infrastructure Controlled System
Infrastructure is a roadside infrastructure unit which is used to
communicate with the vehicles in the coverage zone. Infrastructure based
control of vehicles is considered for this analysis. The method of start, stop
and speed control information is transferred from the roadside infrastructure
unit to the vehicles within the zone. The vehicles in that zone receive this
information and act accordingly to the information received from the
infrastructure. The vehicle acknowledges the infrastructure about the vehicle
motion. Based on this information the roadside infrastructure unit can predict
and control the traffic flow within the zone by communicating with the
vehicles within the zone. The road side infrastructure with communication
module and two vehicles in the track with communication modules are shown
in Figure 5.13 are used for implementing the task.
140
Figure 5.13 Infrastructure Controlled System
5.5.3
Vehicle Platooning
In vehicle platooning the leader vehicle communicates with the
vehicles in the platoon to start/stop or accelerate/decelerate to maintain the
smooth ride in the platoon. In case of emergency, the roadside infrastructure
unit communicates with the vehicle for emergency stop thus proving roadside
infrastructure unit to vehicle communication. The emergency vehicle informs
the infrastructure about the vehicle. Then the infrastructure, signals the other
vehicles intimating the emergency vehicle in the track. Four prototype
vehicles in platoon as shown in Figure 5.14 are used for implementing the
task.
141
Figure 5.14 Four Vehicles in Platoon
5.6
CONCLUSION
This chapter discusses about the survey of the longitudinal control
algorithms which include vehicle speed control, vehicle communication and
vehicle platooning. The proposed longitudinal control algorithms and its
simulation result are analyzed in real time using the prototype vehicles.
Prototype vehicles-1 and 2 are used for testing the proposed
longitudinal control algorithms in real time. The adaptive speed control
algorithm used in prototype-1 uses encoder and prototype-2 uses current
feedback to measure the speed of the vehicle. In the proposed algorithm
current speed of the vehicle is compared with the reference speed and based
on the difference in speed the speed correction is achieved smoothly. This
proposed algorithm makes the vehicle to travel the test bed track with
optimum speed, independent of the load, friction between the tire and the
142
road, etc., Adaptive acceleration control algorithm uses steer error based
speed control where the delta difference between the present and past
deviation is used for acceleration/deceleration which makes the vehicle to
travel in the maximum speed in straight line as well as when the error is
minimum, thus makes the vehicle to complete the desired lap is short duration
of time. The speed variation of the vehicle according to the distance of the
obstacle is discussed. The torque required to propel a vehicle is directly
proportional to the steering angle of the wheel and inversely proportional to
the speed of the vehicle. This condition is achieved by providing steer error
based speed control techniques. Steer error based speed control and critical
speed estimation algorithms improves the performance and provides safety
for the vehicle in motion.
Vehicle
communication
is
established
for
implementing
intersection collision avoidance between vehicles which forms cooperative
vehicle systems. Infrastructure controlled systems, in which the vehicles in
the coverage zone are controlled by the roadside infrastructure unit which
prevents traffic congestion and smooth traffic flow is shown. Vehicle
platooning concepts are discussed with four vehicles in platoon. Thus the
proposed longitudinal control algorithms provide the best speed control for
the proposed vehicles. The vehicle communication systems and vehicle
platooning improves the performance of the autonomous vehicles in the track
and increases the throughput and safety. The consolidated performance of the
proposed sensing algorithms, lateral control algorithms and longitudinal
control algorithms are tested in real time and the test results are plotted and
tabulated, which will be discussed in chapter 6.

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