Robotic Visual Attention Architecture for ADAS in Critical Embedded Systems for Smart Vehicles

Diego Bruno, William D’Abruzzo Martins, Rafael Alceste Berri, Fernando Santos Osório

2025

Abstract

This paper presents the development of a perception architecture for Advanced Driver Assistance Systems (ADAS) capable of integrating (a) external and (b) internal vehicle perception to evaluate obstacles, traffic signs, pedestrians, navigable areas, potholes and deformations in road, as well as monitor driver behavior, respectively. For external perception, in previous works we used advanced sensors, such as the Velodyne LIDAR-64, the Bumblebee 3D camera for object depth analysis, but in this work, focusing on reducing hardware, processing and time costs, we apply 2D cameras with depth estimation generated by the Depth-Anything V2 network model. Internal perception is performed using the Kinect v2 and the Jetson Nano in conjunction with a SVM (Support Vector Machine) model, allowing the identification of driver posture characteristics and the detection of signs of drunkenness, drowsiness or disrespect for traffic laws. The motivation for this system lies in the fact that more than 90% of traffic accidents in Brazil are caused by human error, while only 1% are detected by surveillance means. The proposed system offers an innovative solution to reduce these rates, integrating cutting-edge technologies to provide advanced road safety. This perception architecture for ADAS offers a solution for road safety, alerting the driver and allowing corrective actions to prevent accidents. The tests carried out demonstrated an accuracy of more than 92% for external and internal perception, validating the effectiveness of the proposed approach.

Paper Citation

in Harvard Style

Bruno D., Martins W., Berri R. and Osório F. (2025). Robotic Visual Attention Architecture for ADAS in Critical Embedded Systems for Smart Vehicles. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 871-878. DOI: 10.5220/0013362600003912

in Bibtex Style

@conference{visapp25,
author={Diego Bruno and William Martins and Rafael Berri and Fernando Osório},
title={Robotic Visual Attention Architecture for ADAS in Critical Embedded Systems for Smart Vehicles},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2025},
pages={871-878},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013362600003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - Robotic Visual Attention Architecture for ADAS in Critical Embedded Systems for Smart Vehicles
SN - 978-989-758-728-3
AU - Bruno D.
AU - Martins W.
AU - Berri R.
AU - Osório F.
PY - 2025
SP - 871
EP - 878
DO - 10.5220/0013362600003912
PB - SciTePress