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International Conference on Trending researches in Engineering, Science & Technology-2016
218
OBSTACLE DETECTOR FOR AUTONOMOUS CAR BASED ON BATS
ECHOLOCATION ALGORITHMS
S. Prakasam1, M. Venkatachalam
2 M. Saroja
2, N. Pradheep
1
1Research Scholar, Department of Electronics, Erode Arts and Science College (Autonomous), Erode-638009
2Associate Professor, Department of Electronics Erode Arts and Science College (Autonomous), Erode-638009
Abstract
For the past hundred years, innovation
within the automotive sector has created safer,
cleaner, and more affordable vehicles, but
progress has been incremental. The industry
now appears close to substantial change,
engendered by autonomous, or "self-driving,"
vehicle technologies. This technology offers
the possibility of significant benefits to social
welfare ,saving lives, reducing crashes,
congestion, fuel consumption, and pollution,
increasing mobility for the disabled; and
ultimately improving land use. After surveying
the advantages and disadvantages of the
technology, we proposed bats echolocation
algorithms to increasing performance of
obstacle sensor systems in autonomous car
designs, it’s also reduced cost and resolving
real time application problems.
Keyword: Sensor, Assured Clear Distance
Ahead (ACDA), Bats algorithms,
Introduction:
An implementation of autonomous car
is follows many type of algorithms, the main
focus of these algorithm to maximize the
autonomous cars performs and reduces the
manufacture cost. But most of the studies, the
result shown autonomous car systems
designed more costly or depending on external
support like GPS, Google Map. Current using
systems have more advantages and some
disadvantages also. One of the main
disadvantages is finding obstacles in roads.
Now days we are using ultrasonic sensor,
Electromagnetic sensor, IR sensor, laser and
Radar. The above sensors have some
limitation. In this work we are using the above
sensors together to improving the autonomous
car performance at one step ahead. In this
paper we propose a new algorithm for
autonomous car to find the obstacles based on
bats echolocation optimization techniques. In
this technique the autonomous system works
effectively and more independent. Describing
the diversity of bat echolocation calls, requires
examination of the frequency and temporal
features of that calls. It is the variations in
some aspects that produce echolocation calls
suited for different acoustic environments and
hunting behaviors. As same the new system is
going to produced different frequency in
different time sequence to finding obstacles
and controlling the vehicles by localized
control system. Normally a radar or laser setup
is used that allows the vehicle to slow when
the front vehicle is slowing down and speed up
to the preset speed that the traffic allows when
no vehicle is in front. But those systems are
take action only in straight roads and there is
no capability route diversion weather in this
research paper we tried to offer a fully
autonomous car system. Here we have not
only considered the intelligences on the car but
also in traffic system too. So that an
intelligent network can be established, this is
able to take decision for end to end
communication without any human
interaction.
Sensing Unit Based on Bats Echolocation
Algorithm:
It is really amazing to see the different
techniques used by bats while hunting. The
most common is to capture prey in the air with
the help of the legs and muzzle. Another
clever technique is to unbalance the prey to
provoke a fall so the bat has the chance to pick
it up from the ground. Despite not being
taxonomically related, bats remind us the kind
of sophisticated movements that eagles do
International Conference on Trending researches in Engineering, Science & Technology-2016
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when catching their prey from water. There
are several types of bats with some variations
on their hunting techniques, but some of
them use echolocation as the basic tool for
hunt.
Multi Sensor Techniques in Autonomous
Car:
The bats echolocation algorithm based
on new controller systems design by the multi
sensing. This unit is helps to improve the
performance of obstacle sensing. The
controlling unit is connected with multiple
sensors to identify the obstacles in short and
long ranges. The sensors mainly classified
into two types 1.Short range sensor and 2.
Long range sensor. The multiple sensors unit
controlled all sensors connect in the system.
The short and long ranges ultrasonic and
proximity sensors connected to that unit to
find the obstacle distance.
Types of Obstacle Detection Systems:
Obstacle detection systems usually fall into
the following categories:
Short Range Sensors:
These sensors can be used in vehicles
moving slowly and smoothly towards the
obstacles. If the vehicle stops immediately
upon the detection of obstacle, the sensors
continue to emit a signal of obstacle's present.
If the vehicle resumes its movement, the alarm
signal becomes more prominent as the
obstacle approaches.
Long Range Sensors:
These sensors are highly sophisticated
devices that create sharp radio signals for
detecting the obstacles. They use the echo time
of the radio signals bouncing from the
obstacles to indicate the distance of the
obstacle. Wireless ultrasonic sensors range
from four to eight, placed equally in the front
and rear parts of a vehicle. They detect objects
even when the car is stationary. These sensors
are commonly used during parking the car in a
reverse car parking system. They get activated
as soon as the car is put in reverse gear, and
are usually placed in the front side of a
vehicle.
How Obstacle Detection Systems Work?
An obstacle detection system employs
ultrasonic proximity sensors that are mounted
in front and/or rear bumpers to evaluate the
distance between the vehicle and nearby
objects at low level. These sensors also
measure the time taken for each sound wave to
be reflected back to the receiver.
Obstacle distance identifier:
By analyzing the signal received from
the sensor, the obstacle distance is high means
the vehicle moves ahead with the help of
Autonomous Car Control System (ACCS).
And then the signal transfer to the obstacle
density measurement unit, that the unit checks
if the obstacle is nearer or above the dangerous
level then the system increasing sensing pulse
interval and change the direction of vehicle
International Conference on Trending researches in Engineering, Science & Technology-2016
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using Autonomous Car Control System
(ACCS).
Obstacle Density Measurement:
Density Measurement Unit to
measure the size and density of obstacles. If
the obstacle density is high then Autonomous
Car Control System (ACCS) reduces the
speed, change the direction of vehicle and
analyze the possibilities of next pre
programmed moves.
Sensor Control Unit:
Based on the sensing input we identify
obstacle distance and obstacle density, the
control system increasing the call duration and
pulse interval of sensing unit, its help to
identify the exact distance of obstacle.
Based on the vehicle speed and
obstacle distance, the system will warn the
driver about the collision risk through
audio/visual means. The feedback given to the
driver will indicate the proximity and direction
of the obstacle.
Audible beeps or tones are the most
common forms for a feedback system. The
frequency of the tone represents the distance
from the obstacle, with the tones becoming
faster as the vehicle comes closer to the object.
When a vehicle is very close to the object, the
sensors emit a continuous tone, as a warning to
make the driver stop immediately to avoid
collision.
Assured Clear Distance Ahead:
The Assured Clear Distance
Ahead (ACDA) is the distance ahead of any
terrestrial locomotive device such as a land
vehicle, which can be seen to be clear of
hazards by the driver.
Conclusion:
Now days many advanced
technologies introduced in automobile cars.
The implementation of new sensors and
mechanical systems combined in single
controller can be reduced the processing time
and cost. The vehicle tracking system, Rear-
view alarm to detect obstacles behind, Anti-
lock braking system (ABS), (also Emergency
Braking Assistance (EBA)), often coupled
with Electronic brake force
distribution (EBD), which prevents the brakes
from locking and losing traction while
braking. This shortens stopping distances in
most cases and, more importantly, allows the
driver to steer the vehicle while braking.
Traction control system (TCS) actuates brakes
or reduces throttle to restore traction if driven
wheels begin to spin. Four wheel
drive (AWD) with a centre differential.
Electronic Stability Control (ESC),
Acceleration Slip Regulation (ASR) and
Electronic differential lock (EDL)). Use
various sensors to intervene when the car
senses a possible loss of control. Dynamic
steering response (DSR) corrects the rate
of power steering system to adapt it to
vehicle's speed and road conditions. In this
research, we proposed the new algorithm for
an autonomous car system to combine all the
features into one complete unit. We believe if
our proposal in incorporating with the recent
advancement, then with few years we will find
the fully self dependent automated car.
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