Year: 2025
Type: article
Abstract: Driving Assistance Systems (ADAS) have become integral to enhancing road safety and driver convenience in autonomous and semi-autonomous vehicles. One key component of ADAS is the accurate recognition of traffic signs and traffic lights to assist drivers in following road regulations and improving decision-making. This paper proposes a vehicular control system based on Computer Vision and Deep Learning techniques, designed to recognize traffic signs and lights and provide real-time driving assistance with distance estimation. The system employs Convolutional Neural Networks (CNNs) for traffic sign recognition and a hybrid detection model for traffic light detection, ensuring accuracy in dynamic environments. The proposed system was tested on publicly available datasets and demonstrated significant improvements in detection accuracy and response time, contributing to the development of safer and more efficient autonomous vehicles
Author: Akshay S. Utane
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4407748857