Review cloud platform, we are able to test and

on Autonomous Car Using Raspberry Pi

Supriya K. Kokate 1, M.
H. Nerkar 2

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Mtech. Student, Department of Electronics and
Telecommunication Engineering, Government College of Engineering, Jalgaon,

Associate Professor, Department of Electronics and
Telecommunication Engineering, Government College of Engineering, Jalgaon, India2


ABSTRACT: In this paper we presents an autonomous car (also
called robotic car, driverless car, self-driving car) is a vehicle that is
capable of sensing its environment and navigating without human input. These
systems are consisting of three major subsystems: (1) Algorithms for
localization, perception, and planning and control; (2) Client systems, such as
hardware platform; and (3) The cloud platform, which includes data storage. The
algorithm subsystem extracts meaningful information from sensor and to understand
its environment and make decisions about its actions. The client subsystem
integrates these algorithms to meet real-time and reliability requirements. The
cloud platform provides offline computing and storage capabilities for
autonomous vehicles. Using the cloud platform, we are able to test and train
better recognition, tracking, and decision models. An Autonomous car can be very safe and useful for the
entire mankind. Several software applications process data using Artificial
Intelligence to recognize and propose a path which an intelligent car should
follow. Additionally an autonomous car can detect the distance between the
cars, lowering the degree of road loadings, reducing the number of traffic
jams, avoiding human errors, and allowing disabled people (even blind people)
to drive long distances.


KEYWORDS: Raspberry Pi, Ultrasonic sensor,
camera, lane detection, obstacle detection, GPS


I.      Introduction

in recent years has developed a highly advanced autonomous car. However, when
you begin to take the question what exactly is allowing their car to behave
autonomously you begin to realize that under the advanced algorithms exist
conceptually simple components to make such a thing possible. For example,
object detection can be performed many different ways, through several
different types of sensors such as ultrasonic sensors and obtaining images of
the road is nothing more than a camera taking a photo and then performing image
processing algorithms through software. So, the thought occurs that perhaps
something as simple as a Raspberry Pi and some simple sensors can be used to
help create, learn and evolve an autonomous car that can detect obstacles,
roads and drive along unknown roads on its own. With the intent to help lead to
a future where human driving error can be eliminated and avoid deadly
accidents, injury or deaths.

II.   Literature Survey


In 3 the project mainly focuses on the basis to implement the
object detection and tracking based on its colour, which is a visual based
project i.e., the input to the project will be the video/image data which is
continuously captured with the help of a webcam which is interfaced to the
Raspberry Pi. It will detect the object and it tracks that object by moving the
camera in the direction of the detected object. In 4 the proposed system performed according to its expectation.
The Raspberry pi offers better size but less speed. Accuracy of both systems
was similar even if the FPS rate is very different. Our algorithm can be
implemented to almost any marine environment given the task for which it is
designed. In 5 the
basic detection process
consist of scanning the image lattice and at each location s testing whether
Xs+W is classified as object or background. This is typically done at multiple
resolutions of the image pyramid to detect objects at multiple scales, and is
clearly a very intensive computation. There are a number of methods to make it
more ancient. In 6 these days it is necessary to maintain continuous
surveillance of underwater transmission lines or oil pipelines. For such
purpose, we require an underwater vehicle rover capable of tracking these wires
or pipelines and detect the fault if it occurs. For this purpose we have
designed an intelligent quad leg rover. Image processing as a key deployed for
tracking and tracing the fault or damage. In 7 this paper previous work on
object detection and tracking using UAV’s can be classified into various areas.
Some researchers have focused on implementation of the ‘Follow Me’ mode, in
which, the UAV follows a person. Person has ground control station computer
which transmits its GPS location to the flying UAV. In 8 the proposed system
we can use the background subtraction by using the fixed camera by generating
the foreground mask. It compares the frame with normal one with background
images or model which has contain the static part of the scene, everything is
considered as the background part of images in general. In these back ground
subtraction can be done with the raspberry pi camera.  In 9 this project at the end of the
automatic mode, robot tracks, analyses the colour of the picked object and
drops the object into the respective coloured container. At the end of manual
mode, robot moves and does the task as desired by the user according to the
commands given through the application.



III. functions of autonomous


A vehicle that travels from point A to point B without
any human input for a particular duration of time is classified as an autonomous
vehicle. Such vehicles employ sensory, control and navigation technologies that
respond to the environment accordingly. The U.S. Department of Transportation’s
National Highway Traffic safety Administration (NHTSA) has classified
autonomous vehicles of five levels. The Society for Autonomous Engineers India
(SAE) also has similar classification for autonomous vehicles.

1.       Level 0 (No Automation): The human
driver is in constant and complete control of the car.

2.       Level 1 (Assisted Automation): Only
one function can be automated at a time such as either electronic stability
control, where the vehicle automatically assists with braking. Cruise control,
lane keeping and parking assist are other such common place features found in
autonomous car of this level.

3.       Level 2 (Partial Automation): More
than one function is automated at the same time such as a combination of
adaptive cruise control and lane centering. However, the driver must still
remain constantly attentive.

4.       Level 3 (High Automation): The
functions are sufficiently automated, enabling the driver to safely engage in
other work or activities. The Google car is an example.

5.       Level 4 (Full Automation): The car
can completely drive itself without a human operator. The vehicle is designed
to perform all driving function and monitor roadway conditions for an entire



IV. system architecture and description

The image was taken by the camera which was
placed in the top head of the raspberry pi kit, the camera equipment was
connected via USB port. The capturing image from the camera connected executed
in the Linux OS/Raspbian OS software. The extracted image taken out from the
camera sends to the raspberry pi kit and followed to execution of python
coding. In the python coding the signal are generated, these generated signals
coming from the execution of kit and sent to car/robot. By combination of sixth
sense robotic kit and raspberry pi followed the colour object robot
effectively. The board comes furnished with a SD card. This space licenses us
to embed a SD card and that can utilize it as our gadgets. The SD card is a
fundamental stockpiling gadget for raspberry pi board like a hard plate of a
PC. The bootable Linux working framework is stacked onto the card. The
raspberry pi underpins Linux, ARM, and Mac working frameworks. You can choose
one OS; you should compose it to a SD card utilizing a Disk supervisor application.