通过实时可解释的AI在自动驾驶汽车中提高智能城市的流动性
Ali Zaman Malik1, Naila Samar Naz1, Fahad Ahmed1
1Department of Computer Science, National College of Business Administration and Economics, Lahore, 54000, Pakistan.
Scientific reports
|November 26, 2025
概括
本研究介绍了基于可解释AI (XAI) 的YOLOv5模型,以提高自动驾驶汽车网络 (AVN) 的决策透明度. 这种方法提高了安全性和公众对智慧城市交通系统的信任.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 城市交通系统 城市交通系统
背景情况:
- 自动驾驶汽车网络 (AVN) 为智慧城市提供了变革的潜力,但在决策透明度,公众信任和安全方面面临挑战.
- 现有的AVN开发往往将技术可靠性优先于可解释的决策流程,阻碍了公众的信任和采用.
- 缺乏对自动驾驶汽车 (AV) 如何做出实时决策的理解,阻碍了更广泛地融入城市环境.
研究的目的:
- 开发使用可解释AI (XAI) 的AVN透明和可解释的决策框架.
- 为了提高AVN在智能城市生态系统中的安全性,可靠性和公众接受度.
- 将先进的物体检测与人工智能解释性集成为实时城市移动.
主要方法:
- 将你只看一次,V5 (YOLOv5) 对象检测模型与可解释AI (XAI) 技术集成.
- 基于XAI的YOLOv5模型的开发,用于AV中实时,可解释的决策.
- 评估模型在提高透明度,安全和公众信任方面的表现.
主要成果:
- 拟议的基于XAI的YOLOv5模型实现了99%的准确性,错误率为1%.
- 证明了更高的分类准确性和决定透明度的显著改进.
- 该模型有效地解决了在AVN操作中对可解释AI的需求.
结论:
- 基于XAI的YOLOv5模型为AVN中的透明和可解释的决策提供了一个强大的解决方案.
- 提高透明度和可解释性对于促进公众信任和加速智能城市AVN采用至关重要.
- 这项研究有助于更安全,更可靠,公开接受的自主运输系统.
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