Automatic Car Controller Based on Sign Board using Deep Learning and IOT

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 17 | 02 | Page :
    By

    T. M. Dudhane Guide,

  • Pravin R. Katta,

  • Omkar V. Jagtap,

  • Rushikesh V. Kale,

  1. Assistant Professor, Department of Electronics & Telecommunication Engineering,Rajgad Dnyanpeeth’s Rajgad Technical Campus, Dhangwadi, Bhor, Pune, Maharashtra, India
  2. Students, Department of Electronics & Telecommunication Engineering,Rajgad Dnyanpeeth’s Rajgad Technical Campus, Dhangwadi, Bhor, Pune, Maharashtra, India
  3. Students, Department of Electronics & Telecommunication Engineering,Rajgad Dnyanpeeth’s Rajgad Technical Campus, Dhangwadi, Bhor, Pune, Maharashtra, India
  4. Students, Department of Electronics & Telecommunication Engineering,Rajgad Dnyanpeeth’s Rajgad Technical Campus, Dhangwadi, Bhor, Pune, Maharashtra, India

Abstract

The rapid growth of intelligent transportation systems has increased the demand for safer and more efficient driving solutions. Conventional vehicles often rely heavily on human intervention, which can lead to accidents due to negligence, fatigue, or poor visibility of traffic signs. This project proposes an automated car control system that utilizes deep learning and Internet of Things (IoT) technologies to recognize traffic signboards and respond accordingly. The primary objective is to enhance road safety by enabling vehicles to automatically detect traffic signs and adjust their behavior in real time.The system employs a MobileNet-based convolutional neural network (CNN) for traffic sign recognition due to its lightweight architecture and high efficiency in real- time applications. A dataset of traffic sign images is collected and preprocessed, followed by annotation and training of the model using TensorFlow. The trained model can identify multiple traffic signs under varying environmental conditions such as lighting, angle, and partial obstruction. Once a traffic sign is detected, the system generates appropriate control signals.These signals are transmitted to a microcontroller, which regulates the speed of the vehicle using a motor control mechanism. Additionally, an alert system notifies the driver through audio feedback, ensuring awareness even in semi-automated scenarios. This integration of computer vision and embedded systems enables real-time decision- making and reduces dependency on manual driving responses.The proposed system is cost-effective compared to high-end autonomous driving technologies, making it suitable for wider adoption, espe- cially in developing regions. By combining deep learning with IoT- based control, the system not only improves traffic compliance but also reduces the likelihood of accidents caused by missed or ignored traffic signals. Overall, this approach demonstrates a scalable and practical solution for intelligent vehicle automation.

Keywords: Deep Learning, Traffic Signal, Mobile-Net Neu- ral Network, ESP 32, Blynk IOT.

How to cite this article:
T. M. Dudhane Guide, Pravin R. Katta, Omkar V. Jagtap, Rushikesh V. Kale. Automatic Car Controller Based on Sign Board using Deep Learning and IOT. Journal of Control & Instrumentation. 2026; 17(02):-.
How to cite this URL:
T. M. Dudhane Guide, Pravin R. Katta, Omkar V. Jagtap, Rushikesh V. Kale. Automatic Car Controller Based on Sign Board using Deep Learning and IOT. Journal of Control & Instrumentation. 2026; 17(02):-. Available from: https://journals.stmjournals.com/joci/article=2026/view=247536


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Ahead of Print Subscription Review Article
Volume 17
02
Received 16/04/2026
Accepted 18/06/2026
Published 25/06/2026
Publication Time 70 Days
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