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

翻译：驾驶危险中相当一部分源于人为错误及对当地交通法规的漠视，因此开发智能辅助系统具有重要价值。本文提出一种新颖的基于视觉的模块化套件，通过环境感知保障驾驶员安全。各模块基于精度与推理时间进行设计，以实现实时性能。基于此，本系统可在最低硬件需求条件下部署于多种车辆。该模块化套件包含四个核心部分：车道检测、目标检测、语义分割及单目深度估计。每个部分均辅以创新技术，用于提升其他模块及整体系统精度。此外，系统开发了图形用户界面以向驾驶员展示感知信息。除使用BDD100K等公开数据集外，我们还采集并标注了本地数据集，用于微调与评估系统。实验表明，本系统各模块精度均超过80%。相关代码与数据见https://github.com/Pandas-Team/Autonomous-Vehicle-Environment-Perception。