Year: 2024
Type: article
Abstract: 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.
Authors: Artem Teliuk, Andrii Vasyliuk, Andrii Khudyi
Institution: Lviv Polytechnic National University
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4405651407