An intelligent modular real-time vision-based system for environment perception
Abstract
A significant portion of driving hazards is caused by human error and disregard for local driving regulations; Consequently, an intelligent assistance system can be beneficial. This paper proposes a novel vision-based modular package to ensure drivers' safety by perceiving the environment. Each module is designed based on accuracy and inference time to deliver real-time performance. As a result, the proposed system can be implemented on a wide range of vehicles with minimum hardware requirements. Our modular package comprises four main sections: lane detection, object detection, segmentation, and monocular depth estimation. Each section is accompanied by novel techniques to improve the accuracy of others along with the entire system. Furthermore, a GUI is developed to display perceived information to the driver. In addition to using public datasets, like BDD100K, we have also collected and annotated a local dataset that we utilize to fine-tune and evaluate our system. We show that the accuracy of our system is above 80% in all the sections. Our code and data are available at https://github.com/Pandas-Team/Autonomous-Vehicle-Environment-Perception
Cite
@article{arxiv.2303.16710,
title = {An intelligent modular real-time vision-based system for environment perception},
author = {Amirhossein Kazerouni and Amirhossein Heydarian and Milad Soltany and Aida Mohammadshahi and Abbas Omidi and Saeed Ebadollahi},
journal= {arXiv preprint arXiv:2303.16710},
year = {2023}
}
Comments
Accepted in NeurIPS 2022 Workshop on Machine Learning for Autonomous Driving