DEVELOP AN APPLICATION TO CLASSIFY AND PREDICT THE ENVIRONMENT USING DEEP LEARNING FOR AN AUTONOMOUS VEHICLE

Vishwa Meyyappan M; Pranesh S; Vishnu P; Sundara Murthy S

Important Dates

Submission Last date	28-Jun-2025
Acceptance Status	In One Day
Paper Publish	In Two Days
Submit ManuScript

News for bloggers Submit Article

Dear Authors, Article publish in our journal for Volume-11,Issue-3. For article submission on below link: Submit Manuscript

Join As Board

Dear Reviewer, You can join our Reviewer team without given any charges in our journal. Submit Details on below link: Join As Board

Paper Publication Charges

IJARIIE APP
Download Android App

Download IJARIIE APP

Authors

Title: :  DEVELOP AN APPLICATION TO CLASSIFY AND PREDICT THE ENVIRONMENT USING DEEP LEARNING FOR AN AUTONOMOUS VEHICLE
PaperId: :  21790
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 9 Issue 5 2023
DUI:    16.0415/IJARIIE-21790
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author Name	Author Institute
Vishwa Meyyappan M	Bannari Amman Institute Of Technology
Pranesh S	Bannari Amman Institute Of Technology
Vishnu P	Bannari Amman Institute Of Technology
Sundara Murthy S	Bannari Amman Institute Of Technology

Abstract

Research Area	Computer Science and Engineering
KeyWord	Environment Detection, Deep Learning, Autonomous Vehicles, Object Detection, YOLOv4-tiny, OpenCV, Real- time Analysis, Alert Mechanism, Web Interface.
Abstract	The proposed paper focuses on the development of an innovative application designed for environment detection and prediction utilizing deep learning techniques, tailored specifically for autonomous vehicles. The goal is to create a system that can interpret and predict the surrounding environment from images and videos without relying on external sensors. The system seamlessly integrates real-time analysis from two distinct sources: a webcam feed and a dash-cam feed. Advanced object detection techniques are employed to predict and classify objects in the vehicle's surroundings, providing crucial information for safe autonomous navigation. The application employs YOLOv4-tiny, a state of the art object detection algorithm, to identify and classify objects in the environment and OpenCV is integrated for video and image processing, aiding in frame extraction and preprocessing tasks. The core functionality of the system revolves around real-time vehicle detection and distance estimation. By employing a predefined set of neural network models, utilizing predefined distance constants and object dimensions, the system calculates the distance between the detected vehicles and the camera, ensuring precise spatial awareness. Visual cues are incorporated to highlight application logic processes user inputs, vehicles, and potential hazards are flagged with an alert mechanism if they breach predefined safety thresholds, providing critical information for autonomous vehicle decision making. The application is wrapped within a user-friendly web interface developed using HTML/CSS and the Flask framework, offering two distinct views: one for the webcam feed and another for the dash-cam feed. Users can observe the object detection process in action through interactive and dynamically updating video frames. The interface provides a seamless experience, enhancing the understanding of the environmental analysis performed by the system. This project not only showcases the technical capability of deep learning in real-time object detection but also emphasizes its practical implementation in enhancing autonomous vehicle safety. The system’s design allows for scalability and future integration into autonomous vehicle platforms, contributing to the advancement of self-driving technology and ensuring safer journeys for passengers and pedestrians alike.

Citations

Copy and paste a formatted citation or use one of the links to import into a bibliography manager and reference.

IJARIIE	Vishwa Meyyappan M, Pranesh S, Vishnu P, and Sundara Murthy S. "DEVELOP AN APPLICATION TO CLASSIFY AND PREDICT THE ENVIRONMENT USING DEEP LEARNING FOR AN AUTONOMOUS VEHICLE" International Journal Of Advance Research And Innovative Ideas In Education Volume 9 Issue 5 2023 Page 1808-1813
MLA	Vishwa Meyyappan M, Pranesh S, Vishnu P, and Sundara Murthy S. "DEVELOP AN APPLICATION TO CLASSIFY AND PREDICT THE ENVIRONMENT USING DEEP LEARNING FOR AN AUTONOMOUS VEHICLE." International Journal Of Advance Research And Innovative Ideas In Education 9.5(2023) : 1808-1813.
APA	Vishwa Meyyappan M, Pranesh S, Vishnu P, & Sundara Murthy S. (2023). DEVELOP AN APPLICATION TO CLASSIFY AND PREDICT THE ENVIRONMENT USING DEEP LEARNING FOR AN AUTONOMOUS VEHICLE. International Journal Of Advance Research And Innovative Ideas In Education, 9(5), 1808-1813.
Chicago	Vishwa Meyyappan M, Pranesh S, Vishnu P, and Sundara Murthy S. "DEVELOP AN APPLICATION TO CLASSIFY AND PREDICT THE ENVIRONMENT USING DEEP LEARNING FOR AN AUTONOMOUS VEHICLE." International Journal Of Advance Research And Innovative Ideas In Education 9, no. 5 (2023) : 1808-1813.
Oxford	Vishwa Meyyappan M, Pranesh S, Vishnu P, and Sundara Murthy S. 'DEVELOP AN APPLICATION TO CLASSIFY AND PREDICT THE ENVIRONMENT USING DEEP LEARNING FOR AN AUTONOMOUS VEHICLE', International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 5, 2023, p. 1808-1813. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/DEVELOP_AN_APPLICATION_TO_CLASSIFY_AND_PREDICT_THE_ENVIRONMENT_USING_DEEP_LEARNING_FOR_AN_AUTONOMOUS_VEHICLE_ijariie21790.pdf (Accessed : 03 February 2024).
Harvard	Vishwa Meyyappan M, Pranesh S, Vishnu P, and Sundara Murthy S. (2023) 'DEVELOP AN APPLICATION TO CLASSIFY AND PREDICT THE ENVIRONMENT USING DEEP LEARNING FOR AN AUTONOMOUS VEHICLE', International Journal Of Advance Research And Innovative Ideas In Education, 9(5), pp. 1808-1813IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/DEVELOP_AN_APPLICATION_TO_CLASSIFY_AND_PREDICT_THE_ENVIRONMENT_USING_DEEP_LEARNING_FOR_AN_AUTONOMOUS_VEHICLE_ijariie21790.pdf (Accessed : 03 February 2024)
IEEE	Vishwa Meyyappan M, Pranesh S, Vishnu P, and Sundara Murthy S, "DEVELOP AN APPLICATION TO CLASSIFY AND PREDICT THE ENVIRONMENT USING DEEP LEARNING FOR AN AUTONOMOUS VEHICLE," International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 5, pp. 1808-1813, Sep-Oct 2023. [Online]. Available: https://ijariie.com/AdminUploadPdf/DEVELOP_AN_APPLICATION_TO_CLASSIFY_AND_PREDICT_THE_ENVIRONMENT_USING_DEEP_LEARNING_FOR_AN_AUTONOMOUS_VEHICLE_ijariie21790.pdf [Accessed : 03 February 2024].
Turabian	Vishwa Meyyappan M, Pranesh S, Vishnu P, and Sundara Murthy S. "DEVELOP AN APPLICATION TO CLASSIFY AND PREDICT THE ENVIRONMENT USING DEEP LEARNING FOR AN AUTONOMOUS VEHICLE." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 9 number 5 (03 February 2024).
Vancouver	Vishwa Meyyappan M, Pranesh S, Vishnu P, and Sundara Murthy S. DEVELOP AN APPLICATION TO CLASSIFY AND PREDICT THE ENVIRONMENT USING DEEP LEARNING FOR AN AUTONOMOUS VEHICLE. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2023 [Cited : 03 February 2024]; 9(5) : 1808-1813. Available from: https://ijariie.com/AdminUploadPdf/DEVELOP_AN_APPLICATION_TO_CLASSIFY_AND_PREDICT_THE_ENVIRONMENT_USING_DEEP_LEARNING_FOR_AN_AUTONOMOUS_VEHICLE_ijariie21790.pdf

BibTex

EndNote

RefMan

RefWorks

Number Of Downloads

Last download on 2/3/2024 6:48:21 AM

Save in Google Drive

Similar-Paper

Title

Area of Research

Auther Name

Action

AN AI POWERED MENTAL HEATH AND WELLNESS COMPAION

Computer Science Engineering

Dr . Archana B

Download

PREDICTING GENETIC VARIANTS PATHOGENECITY

Computer Engineering

Syed Arbeena Kausar

Download

ANDROID MALWARE DETECTION USING MACHINE LEARNING TECHNIQUES