Development of Adaptive Cruise Control (ACC) and Lane Keeping Assist (LKA) Systems for Electric Vehicles

Year : 2025 | Volume : 15 | Issue : 03 | Page : 1 6
    By

    K.G. Dharani,

  • Sai Sabarish A,

  • Kishore Kumar K.,

  • Jagathesan N.,

  1. Assistant Professor, Department of Electronics and Communication Engineering, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India
  2. Student, Department of Electronics and Communication Engineering, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India
  3. Student, Department of Electronics and Communication Engineering, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India
  4. Student, Department of Electronics and Communication Engineering, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India

Abstract

This project investigates an IoT-power Adaptive Driver Assistance System (ADAS) crafted to enhance the comfort, efficiency, and reliability of Electric Vehicles (EVs). By seamlessly integrating Adaptive Cruise Control (ACC) and Lane Keeping Assist (LKA), the system aims to metamorphose driving into a secure, more comfortable, and energy-conscious experience using low-priced sensors and microcontrollers. The ACC module relies on ultrasonic sensing element linked to a NodeMCU, which intelligently corrects vehicle speed to maintain a secure following distance. In parallel, the LKA system uses a camera coupled with OpenCV and Python to detect lane boundaries and dominance steering through a servomechanism motor. IoT technology plays a pivotal role in real-time data monitoring, remote diagnostics, and over-the-air updates. Both simulation and on-route tests demonstrate the system’s efficiency in optimizing energy usage, enhancing vehicle control, improving long-distance safety, and reducing driver fatigue. This advanced approach offers a scalable and cost-effective solution tailored for modern electric vehicles (EVs), laying the groundwork for smarter and safer roads. Its integration ensures improved performance and reliability, making it a valuable contribution to future transportation technologies and sustainable mobility solutions aimed at creating a safer and more efficient driving experience for all users.

Keywords: Adaptive Cruise Control, Lane Keeping Assist, IoT, NodeMCU, ultrasonic sensors, OpenCV, MATLAB Simulink I

[This article belongs to Journal of Instrumentation Technology & Innovations ]

How to cite this article:
K.G. Dharani, Sai Sabarish A, Kishore Kumar K., Jagathesan N.. Development of Adaptive Cruise Control (ACC) and Lane Keeping Assist (LKA) Systems for Electric Vehicles. Journal of Instrumentation Technology & Innovations. 2025; 15(03):1-6.
How to cite this URL:
K.G. Dharani, Sai Sabarish A, Kishore Kumar K., Jagathesan N.. Development of Adaptive Cruise Control (ACC) and Lane Keeping Assist (LKA) Systems for Electric Vehicles. Journal of Instrumentation Technology & Innovations. 2025; 15(03):1-6. Available from: https://journals.stmjournals.com/joiti/article=2025/view=227614


References

  1. Hamada S, Kitagawa K. Development of Automated Parking System and Its Applications. Adv Robot Syst Int J. 2007; 4(4): 259–68.
  2. Zhang H, Chen S, Zhao X. Optical Character Recognition for Automatic Vehicle License Plate Recognition. In: IEEE Conf Comput Vis Pattern Recognit Proc. 2016; 243–50.
  3. Chen X, Zhou W, Li A. Smart Parking: IoT-Based Parking Lot Management System. Int J Adv Comput Sci Appl. 2017; 8(6): 232–40.
  4. Raza T, Raza H. Design and Implementation of RFID-Based Vehicle Access Control System. J Comput Appl Int. 2015; 116(9): 20–4.
  5. Anderson J. ADAS-Driven Innovations in Electric Vehicles: A Framework for Sustainable Urban Mobility. Int J Smart Transp Syst. 2025; 12(1): 45–53.
  6. Chada SK, Görges D, Ebert A, Teutsch R, Subramanya SP. Evaluation of the Driving Performance and User Acceptance of a Predictive Eco-Driving Assistance System for Electric Vehicles. IEEE Trans Intell Transp Syst. 2022; 23(8): 11429–38.
  7. Muñoz-Benavent P, Armesto L, Girbés Juan V, Solanes JE. Advanced Driving Assistance Systems for an Electric Vehicle. Int J Adv Robot. 2012; 7(3): 150–8.
  8. Raza T, Raza H. Design of Lightweight Driver-Assistance System for Safe Driving in Electric Vehicles. Sensors. 2019; 19(21): 4761–70.
  9. Chen L, Zhao Y. IoT-Based Adaptive Cruise Control System for Electric Vehicles. IEEE Access. 2024; 12(5): 10234–45.
  10. Singh R, Patel M. Integration of IoT and Machine Learning in ADAS for Electric Vehicles. J Intell Transp Syst. 2023; 17(4): 321–30.
  11. Kumar A, Sharma V. The Role of V2X Communication in Enhancing ADAS for Electric Vehicles. Int J Veh Syst. 2022; 15(2): 198–207.
  12. Ali S, Khan F. Real-Time IoT Data Processing for Autonomous Electric Vehicles. J Automot Technol. 2023; 28(3): 245–53.
  13. Smith JA, Brown LM. Integration of IoT and Machine Learning for Advanced Driver Assistance in Electric Vehicles. J Automot Technol. 2024; 15(2): 123–35.
  14. Garcia P, Lee SH. Real-Time Data Processing in IoT-Enhanced ADAS for Electric Vehicles. Int J Veh Technol. 2023; 22(4): 456–68.
  15. Kumar R, Singh T. Enhancing Electric Vehicle Safety through IoT-Based Driver Assistance Systems. IEEE Trans Intell Transp Syst. 2024; 25(1): 78–89.
  16. Nguyen DT, Chen Y. IoT-Driven Predictive Maintenance in ADAS for Electric Vehicles. Sensors. 2023; 23(5): 1123–35.
  17. Patel A, Zhao Q. Adaptive Cruise Control in Electric Vehicles Using IoT and AI Technologies. IEEE Access. 2024; 12: 3456–67.
Regular Issue Subscription Original Research
Volume 15
Issue 03
Received 26/03/2025
Accepted 24/06/2025
Published 26/07/2025
Publication Time 122 Days
201

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