Authors :
Mantu Bera
Volume/Issue :
Volume 11 - 2026, Issue 9 - September
Google Scholar :
https://tinyurl.com/ym438keu
DOI :
https://doi.org/10.38124/ijisrt/26sep225
Note : A published paper may take 4-5
working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and
ResearchGate.
Abstract :
Autonomous vehicles require intelligent perception and decision-making systems to safely navigate dynamic
environments. This paper presents the design and implementation of an Artificial Intelligence (AI) based robotic car capable
of line following, obstacle avoidance, and traffic light detection using the Arduino Nano 33 BLE Sense microcontroller. The
system integrates traditional sensor-based control with Tiny Machine Learning (TinyML) models deployed directly on the
microcontroller. Infrared sensors are used for lane detection, an ultrasonic sensor is used for obstacle detection, and the
onboard camera module performs traffic light recognition using a lightweight convolutional neural network. The TinyML
model is trained with TensorFlow Lite and deployed with TensorFlow Lite for Microcontrollers. Experimental results show
that the proposed system achieves reliable navigation and traffic signal recognition while maintaining low power
consumption and real-time performance. The proposed architecture demonstrates the feasibility of implementing AI-based
perception systems on resource-constrained embedded hardware.
Keywords :
TinyML, Autonomous Robot Car, Arduino Nano 33 BLE Sense, Obstacle Avoidance, Traffic Light Detection, Line Follower.
References :
- Banbury, C., et al. "TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers."
- Warden, P., Situnayake, D. TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers.
- TensorFlow Lite Micro Documentation.
4. Edge Impulse TinyML Development Platform.
Autonomous vehicles require intelligent perception and decision-making systems to safely navigate dynamic
environments. This paper presents the design and implementation of an Artificial Intelligence (AI) based robotic car capable
of line following, obstacle avoidance, and traffic light detection using the Arduino Nano 33 BLE Sense microcontroller. The
system integrates traditional sensor-based control with Tiny Machine Learning (TinyML) models deployed directly on the
microcontroller. Infrared sensors are used for lane detection, an ultrasonic sensor is used for obstacle detection, and the
onboard camera module performs traffic light recognition using a lightweight convolutional neural network. The TinyML
model is trained with TensorFlow Lite and deployed with TensorFlow Lite for Microcontrollers. Experimental results show
that the proposed system achieves reliable navigation and traffic signal recognition while maintaining low power
consumption and real-time performance. The proposed architecture demonstrates the feasibility of implementing AI-based
perception systems on resource-constrained embedded hardware.
Keywords :
TinyML, Autonomous Robot Car, Arduino Nano 33 BLE Sense, Obstacle Avoidance, Traffic Light Detection, Line Follower.
