Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

5.3K
An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
5.3K
Decision Making01:20

Decision Making

858
Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
858
Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

194
Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
194

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Design and development of a 1-bit dual-mode metasurface for sub-6 GHz wireless communication systems.

Scientific reports·2026
Same author

Automated bone marrow cell classification using ensemble learning: performance, generalization, and clinical interpretability.

Frontiers in medicine·2026
Same author

Multimodal Fusion of Endoscopic and Histopathological Images for Lesion Detection Using Hybrid Deep Learning.

Current medical imaging·2026
Same author

RETRACTED: Srivastava et al. Match-Level Fusion of Finger-Knuckle Print and Iris for Human Identity Validation Using Neuro-Fuzzy Classifier. <i>Sensors</i> 2022, <i>22</i>, 3620.

Sensors (Basel, Switzerland)·2026
Same author

Depression and its associated factors among adult women in Bangladesh during the July 2024 revolution.

Discover mental health·2026
Same author

Convergence of multidrug resistance with biofilm formation and hypermucoviscosity in <i>Klebsiella pneumoniae</i> from tertiary-care hospitals in Northwestern Pakistan.

Antimicrobial stewardship & healthcare epidemiology : ASHE·2026

相关实验视频

Updated: Jul 16, 2026

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

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
PubMed
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.

关键词:
自动驾驶汽车网络 (AVN) 是一种自动驾驶汽车网络.可解释的人工智能 (XAI)基础设施到基础设施 (I2I)车辆到基础设施 (V2I)车辆对车辆 (V2V) 的关系

相关实验视频

Last Updated: Jul 16, 2026

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

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.