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  3. Volume 16, 2024
  4. Intelligent driver assistance systems based on computer vision and deep learning

Intelligent driver assistance systems based on computer vision and deep learning

SISN.
2024;
Volume 16
: pp. 303 - 324
https://doi.org/10.23939/sisn2024.16.303
Authors:
  1. Artem Teliuk
  2. Andrii Vasyliuk
  3. Andrii Khudyi
1
Lviv Polytechnic National University, Department of Information Systems and Networks
2
Lviv Polytechnic National University, Department of Information Systems and Networks
3
Lviv Polytechnic National University, Information Systems and Networks Department

This article presents an integrated Advanced Driver Assistance System (ADAS) that combines several key functional modules, such as collision warning, lane detection, traffic sign recognition, and pothole detection, which are implemented using modern deep learning models, particularly YOLOv8n. The system is optimized for devices with limited computational resources, such as Raspberry Pi or NVIDIA Jetson Nano, by employing a modular architecture and parallel data processing to ensure realtime performance. This research provides an overview of existing ADAS solutions and proposes new approaches that significantly enhance the efficiency of such systems. Key innovations include an efficient approach to lane detection based on object detection models, real-time traffic sign recognition with a flexible extraction and classification process, and a novel pothole detection system optimized for dashcam recordings. Additionally, the proposed driver alert system, which uses an LED strip, allows for intuitive hazard awareness without distracting the driver. Preliminary results confirm satisfactory detection accuracy across all components, although further optimization is required for successful deployment on low-resource devices

intelligent driver assistance system
object detection
deep learning
real-time
YOLOv8n
lane detection
traffic signs
potholes.
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