Journal of Electrical Engineering and Automation
IRO Journals
Volume 7 — Issue 1 — March 2025

Enhancing Safe and Precise Maneuvering in Autonomous Electric Vehicle

https://doi.org/10.36548/jeea.2025.1.002
Ashwin S.
Department of Electrical and Electronics Engineering, Coimbatore Institute of Technology, Coimbatore, India
Nafila B.
Department of Electrical and Electronics Engineering, Coimbatore Institute of Technology, Coimbatore, India
Sajana S.
Department of Electrical and Electronics Engineering, Coimbatore Institute of Technology, Coimbatore, India
Sutharsan S.
Department of Electrical and Electronics Engineering, Coimbatore Institute of Technology, Coimbatore, India
Arthi K.
Department of Electrical and Electronics Engineering, Coimbatore Institute of Technology, Coimbatore, India
PDF
PDF

How to Cite

S., Ashwin, Nafila B., Sajana S., Sutharsan S., and Arthi K. 2025. “Enhancing Safe and Precise Maneuvering in Autonomous Electric Vehicle”. Journal of Electrical Engineering and Automation 7 (1): 13-26. https://doi.org/10.36548/jeea.2025.1.002.
ACM ACS APA ABNT Chicago Harvard IEEE MLA Turabian Vancouver AMA

Download Citation

Endnote/Zotero/Mendeley (RIS) BibTeX

Keywords

— Autonomous Driving
— Obstacle Detection
— Master-Slave Communication
Published: 07-04-2025

Abstract

This research deals with the design of an autonomous vehicle navigation system based on a master-slave computational paradigm, combining Raspberry Pi 4 and Arduino UNO for visual perception in real-time, decision-making, and actuation. The Raspberry Pi 4, supported by a Pi Camera module, performs lane detection and near-object identification through image processing techniques, and then sends over extracted data through serial communication to Arduino UNO. Contrary to traditional autonomous driving systems that are based largely on monolithic processing architectures, this two-part control paradigm maximally splits computational loads, thus maximizing real-time responsiveness and efficiency of computation. The approach coordinates an optimal interaction between image-based navigation and multi-sensor fusion to guarantee improved trajectory planning and obstacle avoidance. In addition, the servo-actuated ultrasonic module provides an extended spatial detection range over traditional static-sensor deployments, thus allowing for better environmental adaptability, especially in small or dynamically changing environments.

References

  1. Jin, Ping-Fan, Jung-Hyun Kim, and Jung-Ha Kim. "Design of unmanned vehicle advanced braking system using smart motor." In 2015 12th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), IEEE, 2015. 167-171.
  2. 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.
  3. Rehman, Abbad Ur, Zohaib Mushtaq, and Muhammad Attique Qamar. "Fuzzy logic based automatic vehicle collision prevention system." In 2015 IEEE Conference on Systems, Process and Control (ICSPC), IEEE, 2015. 55-60.
  4. Fenesan, Andrei, Teodor Pana, Daniel Szöcs, and Wen-Hua Chen. "Building an electric model vehicle and implementing an obstacle avoidance algorithm." In 2012 International Conference and Exposition on Electrical and Power Engineering, IEEE, 2012. 49-53.
  5. Freschi, Fabio, Massimo Mitolo, and Riccardo Tommasini. "Electrical safety of electric vehicles." In 2017 IEEE/IAS 53rd industrial and commercial power systems technical conference (I&CPS), IEEE, 2017. 1-5.
  6. Kristan, Matej, Vildana Sulić Kenk, Stanislav Kovačič, and Janez Perš. "Fast image-based obstacle detection from unmanned surface vehicles." IEEE transactions on cybernetics 46, no. 3 (2015): 641-654.
  7. Enisz, Krisztian, Denes Fodor, Istvan Szalay, and Gabor Kohlrusz. "Improvement of active safety systems by the extended Kalman filter based estimation of tire-road friction coefficient." In 2014 IEEE International Electric Vehicle Conference (IEVC), IEEE, 2014. 1-5.
  8. Zhao, Linhui, and Zhiyuan Liu. "Vehicle state estimation with friction adaptation for four-wheel independent drive electric vehicle." In Proceeding of the 11th World Congress on intelligent Control and Automation, IEEE, 2014. 4527-4531.
  9. Ariyanto, Mochammad, Gunawan D. Haryadi, Munadi Munadi, Rifky Ismail, and Zulfa Hendra. "Development of low-cost autonomous emergency braking system (AEBS) for an electric car." In 2018 5th International Conference on Electric Vehicular Technology (ICEVT), IEEE, 2018. 167-171.
  10. Mariaraja, P., T. Manigandan, S. Arun, RM Meenakshi Sundaram, and K. Dhenesh. "Design and implementation of advanced automatic driving system using raspberry pi." In 2020 5th International Conference on Communication and Electronics Systems (ICCES), IEEE, 2020. 1362-1367
  11. AC, Ramachandra. "Advanced Driving Assistance System for Cars Using Raspberry Pi." Indian Journal of Science and Technology 15, no. 40 (2022): 2056-2065.
  12. McCann, Roy A., and Anh T. Le. "A new multisensor network for collision avoidance and jackknife prevention of articulated vehicles using Lebesgue sampling." In 2005 IEEE Vehicle Power and Propulsion Conference, IEEE, 2005.
  13. Zhiwei, Song, Huang Weiwei, Wu Ning, Wu Xiaojun, Wong Chern Yuen Anthony, Vincensius Billy Saputra, Benjamin Chia Hon Quan et al. "Map free lane following based on low-cost laser scanner for near future autonomous service vehicle." In 2015 IEEE Intelligent Vehicles Symposium (IV), IEEE, 2015. 706-711.
  14. Madhavan, T. R., and M. Adharsh. "Obstacle detection and obstacle avoidance algorithm based on 2-D RPLiDAR." In 2019 International Conference on Computer Communication and Informatics (ICCCI), IEEE, 2019. 1-4.
  15. Zhang, Ying, Zhaoyang Ai, Jinchao Chen, Tao You, Chenglie Du, and Lei Deng. "Energy-saving optimization and control of autonomous electric vehicles with considering multiconstraints." IEEE Transactions on Cybernetics 52, no. 10 (2021): 10869-10881.
  16. Yim, Young Uk, and Se-Young Oh. "Three-feature based automatic lane detection algorithm (TFALDA) for autonomous driving." IEEE Transactions on Intelligent Transportation Systems 4, no. 4 (2003): 219-225.