使用支向量机来分类道路表面状况,以促进安全驾驶
Jaepil Moon1, Wonil Park1,2
1Department of Highway & Transportation Research, Korea Institute of Civil Engineering and Building Technology, Goyang 10223, Republic of Korea.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
这项研究开发了一个数据驱动的学习模型,使用支持矢量机 (SVM) 来准确地检测冬季道路表面状况. 该模型有效地估计了条件,提高了交通安全和道路管理.
科学领域:
- 计算机科学 计算机科学
- 工程 工程师 工程师 工程师
- 环境科学 环境科学
背景情况:
- 冬季准确检测路面状况对于交通安全和有效的道路管理至关重要.
- 在恶劣的天气条件下,现有的方法可能缺乏准确性或通用性.
研究的目的:
- 开发和评估数据驱动的学习模型,以准确和可概括地估计道路表面状况.
- 评估不同内核功能和分类策略的支持矢量机 (SVM) 模型的性能.
主要方法:
- 使用了支持向量机 (SVM) 机器学习模型,具有线性,高斯式和二次多项式内核函数.
- 采用软边缘分类和两个学习者设计 (一对一,一对所有) 用于多类分类.
- 使用sigmoid函数计算后面概率来分析分类信心.
主要成果:
- 大多数SVM分类器的分类错误低于3%,表明在识别道路表面状况时的高准确性.
- 一对一学习者在4%的错误率内表现出概括性能.
- 后期概率有效地确定了与危险情况相关的大气和道路表面条件.
结论:
- 数据驱动的学习模型,特别是SVM,显示出在恶劣的冬季天气中准确分类道路表面状况的巨大潜力.
- 开发的模型有助于改善交通安全和积极的道路管理策略.
- 后续的概率分析提高了危险条件的解释性和预测能力.
关键词:
在SVM中,SVM是SVM.恶劣的冬季天气条件数据驱动的学习模式线性分类器是一个线性分类器.非线性分类器是一个非线性分类器.后面的概率是后面的概率道路表面状况 道路表面状况交通安全 交通安全 交通安全更多相关视频
07:05Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
Published on: October 27, 2016
9.2K
11:12Driving Simulation in the Clinic: Testing Visual Exploratory Behavior in Daily Life Activities in Patients with Visual Field Defects
Published on: September 18, 2012
17.4K
相关概念视频
Classification of Systems-I
179
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
179
Classification of Systems-II
139
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
139
Classification of Signals
432
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
432
Response Surface Methodology
116
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
The process of RSM involves several key steps:
116
Design Example: Alignment of a Road Line Using GIS
47
The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
47
Sight Distance in a Vertical Curve
43
Sight distance on vertical curves is critical in roadway design. It ensures drivers can see far enough ahead to identify and respond to hazards effectively. This directly impacts safety, driver comfort, and the overall efficiency of the transportation network.Vertical curves are classified into crest and sag curves based on their geometry. For crest curves, sight distance is determined by the line of sight between a driver's eye and a small object on the road's surface. Design parameters for...
43
