提出混合视觉架构，提升自动驾驶在恶劣环境下的检测准确率。
Enhanced Drift-Aware Computer Vision Architecture for Autonomous Driving
- 用合成数据训练双模式模型，先快速检测再逐层验证
- 在漂移图像上检测准确率提升超90%
- 适合关注自动驾驶安全与鲁棒性的研究者
计算机视觉在汽车领域的应用日益重要，安全与可靠性是核心关切。针对自动驾驶中复杂路况导致的误检问题，国际标准化组织（ISO）发布了8800标准，规范AI相关风险管理体系。然而，恶劣天气或低光照等场景常引发数据漂移，造成模型性能下降甚至安全隐患。本文提出一种新型混合视觉架构，基于数千张道路环境的合成图像进行训练，以增强对未见漂移环境的鲁棒性。系统采用YOLOv8实现快速检测，并引入五层CNN进行逐级验证，按序运行。在含漂移增强的道路图像测试中，检测准确率提升超过90%。结果表明，该混合结构能有效提升道路安全水平。
原文摘要 · Abstract (English)
The use of computer vision in automotive is a trending research in which safety and security are a primary concern. In particular, for autonomous driving, preventing road accidents requires highly accurate object detection under diverse conditions. To address this issue, recently the International Organization for Standardization (ISO) released the 8800 norm, providing structured frameworks for managing associated AI relevant risks. However, challenging scenarios such as adverse weather or low lighting often introduce data drift, leading to degraded model performance and potential safety violations. In this work, we present a novel hybrid computer vision architecture trained with thousands of synthetic image data from the road environment to improve robustness in unseen drifted environments. Our dual mode framework utilized YOLO version 8 for swift detection and incorporated a five-layer CNN for verification. The system functioned in sequence and improved the detection accuracy by more than 90\% when tested with drift-augmented road images. The focus was to demonstrate how such a hybrid model can provide better road safety when working together in a hybrid structure.
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