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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

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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

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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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