Authors :
H.R Sridhar Kumar; Sudarshan Gurupad Hegde; Sudarshan Adiga; Deepak S Hugar; Goutam S
Volume/Issue :
Volume 8 - 2023, Issue 5 - May
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/sshf47ty
DOI :
https://doi.org/10.5281/zenodo.8041890
Abstract :
with the rapid advancements in autonomous
driving technology, there is a growing interest in
developing cost-effective and efficient solutions. This
paper presents the development of an independent car
system using Raspberry Pi, Arduino and the integration
of the CAN bus protocol for efficient communication with
microcontrollers. This project aims to create a self-
driving vehicle to move from the source point to the
destination point autonomously. The methodology
involves utilizing Raspberry Pi as the image processing
unit, responsible for processing video from pi camera,
making decisions, and sending signals to the Arduino via
can module. The GPS module is employed with Arduino
to get the location information and control the vehicle.
The CAN bus protocol is employed for seamless
communication. The system incorporates a range of
sensing components, including pi cameras, ultrasonic
sensors, GPS, and a compass to gather real-time
environmental data. Machine learning algorithms are
employed for decision-making and control, allowing the
car to navigate and respond to different traffic scenarios.
Experimental showcases the system's capability to detect
and avoid obstacles, follow traffic lights, and follow the
given pathway. The presented solution exhibits promising
advancements in autonomous driving technology using
affordable and accessible hardware components.
This project contributes to the field of autonomous
vehicles by providing a scalable and adaptable
framework using Raspberry Pi and the CAN bus
protocol. Integrating these technologies offers a cost-
effective and efficient solution for developing autonomous
cars. Future work involves enhancing the system's
robustness, optimizing its performance, and addressing
regulatory and safety considerations.
Keywords :
Autonomous car, Raspberry Pi, GPS, Arduino, Compass, CAN bus protocol, Decision-making, Machine learning.
with the rapid advancements in autonomous
driving technology, there is a growing interest in
developing cost-effective and efficient solutions. This
paper presents the development of an independent car
system using Raspberry Pi, Arduino and the integration
of the CAN bus protocol for efficient communication with
microcontrollers. This project aims to create a self-
driving vehicle to move from the source point to the
destination point autonomously. The methodology
involves utilizing Raspberry Pi as the image processing
unit, responsible for processing video from pi camera,
making decisions, and sending signals to the Arduino via
can module. The GPS module is employed with Arduino
to get the location information and control the vehicle.
The CAN bus protocol is employed for seamless
communication. The system incorporates a range of
sensing components, including pi cameras, ultrasonic
sensors, GPS, and a compass to gather real-time
environmental data. Machine learning algorithms are
employed for decision-making and control, allowing the
car to navigate and respond to different traffic scenarios.
Experimental showcases the system's capability to detect
and avoid obstacles, follow traffic lights, and follow the
given pathway. The presented solution exhibits promising
advancements in autonomous driving technology using
affordable and accessible hardware components.
This project contributes to the field of autonomous
vehicles by providing a scalable and adaptable
framework using Raspberry Pi and the CAN bus
protocol. Integrating these technologies offers a cost-
effective and efficient solution for developing autonomous
cars. Future work involves enhancing the system's
robustness, optimizing its performance, and addressing
regulatory and safety considerations.
Keywords :
Autonomous car, Raspberry Pi, GPS, Arduino, Compass, CAN bus protocol, Decision-making, Machine learning.
