Journal of ISMAC
IRO Journals
Volume 7 — Issue 3 — September 2025

Self-Driving Car Using Image Processing

https://doi.org/10.36548/jismac.2025.3.004
Pages: 293-308
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Abhishek Kumar Shukla
Students, Department of Electronics and Computer Engineering, Pashchimanchal Campus
Dhananjay Kumar Yadav
Students, Department of Electronics and Computer Engineering, Pashchimanchal Campus
Prachi Singh Rajput
Students, Department of Electronics and Computer Engineering, Pashchimanchal Campus
Sittal Bhusal
Students, Department of Electronics and Computer Engineering, Pashchimanchal Campus
Suraj Basant Tulachan
Assistant Professor, Department of Electronics and Computer Engineering, Pashchimanchal Campus
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How to Cite

Shukla, Abhishek Kumar, Dhananjay Kumar Yadav, Prachi Singh Rajput, Sittal Bhusal, and Suraj Basant Tulachan. 2025. “Self-Driving Car Using Image Processing”. Journal of ISMAC 7 (3): 293-308. https://doi.org/10.36548/jismac.2025.3.004.
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Keywords

— Self-Driving Car
— Raspberry Pi
— Embedded Systems
— Lane Detection
— Traffic Sign and Light Recognition
Published: 07-10-2025

Abstract

Autonomous cars represent a new mode of transportation using hardware and software to facilitate autonomous driving without human intervention. This is a design document for the creation of a low-cost autonomous vehicle prototype based on the Raspberry Pi 3 as the core processor. The system combines lane detection, traffic light identification, and traffic sign recognition into a single multi-threaded pipeline that facilitates real-time multi-task perception. Experimental testing demonstrates that the prototype attains a mean accuracy of 87%, a processing rate of 13–14 frames per second, and a mean decision time of approximately 105 ms in outdoor and indoor environments. In contrast to earlier Raspberry Pi-based prototypes that executed tasks sequentially or merely followed lanes, the system exhibits the novelty of stable multi-task perception with limited resources. The findings indicate the potential and limitations of embedded systems in autonomous navigation, with possible future applications in transportation, industrial automation, and surveillance.

References

  1. Viswanathan, Vidya, and Rania Hussein. "Applications of image processing and real-time embedded systems in autonomous cars: a short review." International Journal of Image Processing (IJIP) 11, no. 2 (2017): 35.
  2. Borenstein, Johann, and Yoram Koren. "Obstacle avoidance with ultrasonic sensors." IEEE Journal on Robotics and Automation 4, no. 2 (2002): 213-218.
  3. Blyth, Pascale-L., Milos N. Mladenovic, Bonnie A. Nardi, Norman M. Su, and Hamid R. Ekbia. "Driving the self-driving vehicle: Expanding the technological design Horizon." In 2015 IEEE International Symposium on Technology and Society (ISTAS), IEEE, 2015, 1-6.
  4. McCall, Joel C., and Mohan M. Trivedi. "Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation." IEEE transactions on intelligent transportation systems 7, no. 1 (2006): 20-37.
  5. Fagnant, Daniel J., and Kara Kockelman. "Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations." Transportation Research Part A: Policy and Practice 77 (2015): 167-181.
  6. Pawar, Narayan Pandharinath, and Minakshee M. Patil. "Driver assistance system based on Raspberry Pi." International Journal of Computer Applications 95, no. 16 (2014).
  7. Shahane, Vikrant, Hrushikesh Jadhav, Mihir Sansare, and Prathmesh Gunjgur. "A self-driving car platform using raspberry Pi and Arduino." In 2022 6th International Conference On Computing, Communication, Control And Automation (ICCUBEA, IEEE, 2022, 1-6.
  8. Isherwood, Zach, and Emanuele Lindo Secco. "A Raspberry Pi computer vision system for self-driving cars." In Science and Information Conference, Cham: Springer International Publishing, 2022, 910-924.
  9. Bechtel, Michael G., Elise McEllhiney, Minje Kim, and Heechul Yun. "Deeppicar: A low-cost deep neural network-based autonomous car." In 2018 IEEE 24th international conference on embedded and real-time computing systems and applications (RTCSA), IEEE, 2018, 11-21.
  10. Ravindran, Vijay, S. Chandrika, Ram Prakash Ponraj, C. Krishnakumar, S. Devadharshini, and S. Lakshmi. "Autonomous Delivery Vehicle Using Raspberry Pi and Computer Vision." In International Conference on Paradigms of Communication, Computing and Data Analytics, Singapore: Springer Nature Singapore, 2023, 481-493.
  11. Kumar, HR Sridhar, and Sudarshan Gurupad Hegde. "Autonomous Car Using Raspberry Pi and Can Bus Protocol." International Journal of Innovative Science and Research Technology 8, no. 5 (2023).
  12. Golabhavi, P., and B. Harish. "Self-driving car model using raspberry pi." International Journal of Engineering Research & Technology 9, no. 2 (2020): 176-180.
  13. Wagdy, Mahmoud, Ahmed Roushdy, Ahmed Sabry, Abdallah Mostafa, Mahmoud Magdy, and Amal S. Mehanna. "Autonomous Parallel Parking Using Raspberry-PI." In International Conference on Advanced Technologies for Humanity, Cham: Springer International Publishing, 2021, 19-28.
  14. Wu, Dong, Man-Wen Liao, Wei-Tian Zhang, Xing-Gang Wang, Xiang Bai, Wen-Qing Cheng, and Wen-Yu Liu. "Yolop: You only look once for panoptic driving perception." Machine Intelligence Research 19, no. 6 (2022): 550-562.
  15. Kozłowski, Michał, Szymon Racewicz, and Sławomir Wierzbicki. "Image analysis in autonomous vehicles: A review of the latest AI solutions and their comparison." Applied Sciences 14, no. 18 (2024): 8150.
  16. Gajjar, Henil, Stavan Sanyal, and Manan Shah. "A comprehensive study on lane detecting autonomous car using computer vision." Expert Systems with Applications 233 (2023): 120929.
  17. Canny, John. "A computational approach to edge detection." IEEE Transactions on pattern analysis and machine intelligence 6 (2009): 679-698.
  18. Mo, Tiande, Siqian Zheng, Wai-Yat Chan, and Renhua Yang. "Review of AI image enhancement techniques for in-vehicle vision systems under adverse weather conditions." World Electric Vehicle Journal 16, no. 2 (2025): 72. Zhang, Yuxiao, Alexander Carballo, Hanting Yang, and Kazuya Takeda. "Perception and sensing for autonomous vehicles under adverse weather conditions: A survey." ISPRS Journal of Photogrammetry and Remote Sensing 196 (2023): 146-177.
  19. Cheng, Hong. Autonomous intelligent vehicles: theory, algorithms, and implementation. Springer Science & Business Media, 2011.
  20. Fathy, Mahmoud, Nada Ashraf, Omar Ismail, Sarah Fouad, Lobna Shaheen, and Alaa Hamdy. "Design and implementation of self-driving car." Procedia Computer Science 175 (2020): 165-172.
  21. Woo, Seokkyun, Jan Youtie, Ingrid Ott, and Fenja Scheu. "Understanding the long-term emergence of autonomous vehicles technologies." Technological Forecasting and Social Change 170 (2021): 120852.
  22. Zhang, Jingyu, Jin Cao, Jinghao Chang, Xinjin Li, Houze Liu, and Zhenglin Li. "Research on the application of computer vision based on deep learning in autonomous driving technology." In INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, NETWORKING AND APPLICATIONS, Singapore: Springer Nature Singapore, 2023, 82-91.
  23. Miller, Tymoteusz, Irmina Durlik, Ewelina Kostecka, Piotr Borkowski, and Adrianna Łobodzińska. "A critical AI view on autonomous vehicle navigation: The growing danger." Electronics 13, no. 18 (2024): 3660.
  24. Du, Mingfang. Autonomous Vehicle Technology. Springer, 2023.
  25. Janai, Joel, Fatma Güney, Aseem Behl, and Andreas Geiger. "Computer vision for autonomous vehicles: Problems, datasets and state of the art." Foundations and trends® in computer graphics and vision 12, no. 1–3 (2020): 1-308.