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相关概念视频

Fundamental Attribution Error01:14

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According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
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Behavior is a product of both the situation (e.g., cultural influences, social roles, and the presence of bystanders) and of the person (e.g., personality characteristics). Subfields of psychology tend to focus on one influence or behavior over others. Situationism is the view that our behavior and actions are determined by our immediate environment and surroundings. In contrast, dispositionism holds that our behavior is determined by internal factors (Heider, 1958).
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The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
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A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...
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基于形式的事故责任归因框架:可解释的分析与交通事故特征.

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概括

一个新的深度学习模型AMFormer (算术特征交互变压器) 准确地预测了交通事故责任. 它使用时空建模和SHAP分析进行透明,可靠的归因,优于传统方法.

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科学领域:

  • 人工智能的人工智能
  • 运输工程 运输工程
  • 数据科学数据科学数据科学

背景情况:

  • 交通事故责任确定传统上依赖主观证据,导致潜在的偏见和不准确性.
  • 现有的方法缺乏客观性,并与事故场景的复杂性作斗争.

研究的目的:

  • 引入AMFormer (算术特征交互变压器),这是一个新的深度学习框架,用于准确和可解释的交通事故责任预测.
  • 提高客观性,减少对交通事故的责任分配方面的偏见.

主要方法:

  • 开发了AMFormer,这是一个深度学习模型,利用时空特征建模来捕捉复杂的因子相互作用.
  • 应用SHAP (夏普利添加式解释) 分析,用于特征归属和透明度.
  • 在真实世界的交通事故数据集上验证了模型.

主要成果:

  • 在预测事故责任方面,AMFormer实现了93.46%的准确性和93%的F1-Score.
  • 该模型与传统方法和其他深度学习方法相比,表现优越.
  • SHAP分析确定了影响责任赋予及其综合效应的关键特征.

结论:

  • AMFormer为交通事故责任预测提供了一个强大的和可解释的解决方案.
  • 该框架提高了责任分配的可信度,并对自动驾驶汽车的安全产生影响.
  • 这项研究为交通安全分析中更客观和数据驱动的方法提供了基础.