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Design and Prototype of an Autonomous Car using Image Processing

Dinesh Burande, Samarjeet G. Chavan, Rohit K. Jagadale, Aryan D. Gokhale, Sushant S. Patil


The goal of the project is to construct a working model of an autonomous vehicle. The car will receive the essential information from the outside world through the use of an HD camera for image processing. The car can drive itself safely and intelligently to the specified location, reducing the possibility of human error. Numerous currently used methods, like object and lane identification, are combined to offer the car the necessary control. They will test the vehicle object detection using a yellow cheerful ball, and as it advances, we hope to add traffic sign detection. In order to operate on a specific road condition, the car will automatically provide steering recommendations for lane detection. In this way with the help of advanced technology the car can achieve object detection and lane detection autonomously.


Autonomous car, Object detection, Lane detection, Machine learning, Image processing, Steering prediction, Raspberry Pi, Prototype, Artificial Intelligence.

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Chen, Z.; Chen, K.; Chen, J. Vehicle and Pedestrian Detection Using Support Vector Machine and

Histogram of Oriented Gradients Features. In Proceedings of the 2013 International Conference on

Computer Sciences and Applications, Wuhan, China, 14–15 December 2013; pp. 365–368.

Indian Journal of Science and Technology April - May Jun 2016 '' Vision Based Object Detection and

Tracking Using Multi Rotor Unmanned Aerial Vehicle '' Sarthak kaingade, Vikrant More, Dhiraj dhule,

Pradeep gaidhani, Nitin Gupta.

Poojakharade, Laxmimandalollu, Pooja A.S, Poojasavadatti, Mr. Kotreshmarali.”Prototype

Implementation Of Iot Based Autonomous Vehicle On Raspberry Pi ”Bonfring International Journal Of

Research In Communication Engineering, Vol. 6, Special Issue November 2016 .

Gary Bradski, Adrian Kaehler, Learning Opencv: Computer Vision With The Open Cv Library,

"O'reilly Media, Inc.". Copyright. September 2008, 1st Edition Book.

Tan-Hung Duong, Sun-Tae Chung, Seongwon Cho, Model-Based Robust Lane Detection For Driver

Assistance, (Iciiecs) 2005.

R. Cucchiara, C. Grana, M. Piccardi& A. Prati, Detecting Moving Objects, Ghosts, And Shadows In

Video Streams, Ieee Transactions On Pattern Analysis And Machine Intelligence(Pami), Vol. 25(10),

- 1342, 2003.

S. Wang, J. Cheng, H. Liu, F. Wang, and H. Zhou, “Pedestrian detection via body part semantic and

contextualinformation with DNN,” IEEE Trans. Multimedia, vol. 20, no. 11, pp. 3148–3159, Nov. 2018.

Trifan, A., Neves, A.J.R. & Cunha, B, Evaluation Of Color Spaces For User-Supervised Color

Classification In Robotic Vision.17th International Conference On Image Processing, Computer Vision,

& Pattern Recognition, Las Vegas, Nevada, Usa (July 2013).

W. Ouyang, H. Zhou, H. Li, Q. Li, J. Yan, and X. Wang, “Jointly learning deep features, deformable

parts, occlusion and classification for pedestrian detection,” IEEE Trans. Pattern Anal. Mach. Intell.,vol.

, no. 8, pp. 1874–1887, Aug. 2018.

Hordur K. Heidarsson And Gaurav S. Skhatme, “Obstacle Detection And Avoidance For An

Autonomous Surface Vehicle Using A Profiling Sonar” Ieee May 9-13, 2011.



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