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相关概念视频

Manipulation and Analysis01:21

Manipulation and Analysis

26
GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
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Design Example: Alignment of a Road Line Using GIS01:17

Design Example: Alignment of a Road Line Using GIS

49
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...
49
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

27
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
27
Levels of Use of a GIS01:29

Levels of Use of a GIS

53
Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
53
Root-Locus Method01:19

Root-Locus Method

157
A cruise control system in a car is designed to maintain a specified speed automatically by adjusting the gas pedal. The system continuously measures the vehicle's speed and makes fine adjustments to the pedal to achieve this goal. The root locus method is particularly useful for understanding how the cruise control system's behavior changes under varying conditions, such as when the car goes uphill, downhill, or faces strong wind resistance.
This system can be represented by a block...
157
Inductive Reasoning00:59

Inductive Reasoning

60.5K
Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...
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相关实验视频

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A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
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从导航地图的POI推断驾驶环境:模糊逻辑和机器学习方法.

Yu Li1,2, Martin Metzner1, Volker Schwieger1

  • 1Institute of Engineering Geodesy, University of Stuttgart, Geschwister-Scholl-Str. 24D, 70174 Stuttgart, Germany.

Sensors (Basel, Switzerland)
|November 25, 2023
PubMed
概括

这项研究有效地推断了使用 Point of Interest (POI) 数据来更好地控制车辆的驾驶环境. 多层感知器 (MLP) 模型取得了最佳结果,展示了自动驾驶系统的实用方法.

关键词:
驾驶环境推断 推断 推断一个模糊的推断系统.多个标签分类的分类.多层感知器多层感知器导航地图导航地图感兴趣的地方 (POI)支持矢量机器的支持矢量机器.

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科学领域:

  • 智能运输系统 智能运输系统
  • 机器学习用于环境感知.

背景情况:

  • 预测性车辆控制需要了解驾驶环境.
  • 现有的方法在实时上下文推断中可能缺乏效率.

研究的目的:

  • 有效地推断出五个关键的驾驶环境:购物,旅游,公共车站,汽车服务和安全区.
  • 使用 Point of Interest (POI) 数据开发一个强大的推断框架.
  • 为了评估驾驶环境分类的不同推断系统.

主要方法:

  • 利用导航地图上的兴趣点 (POI) 数据作为语义线索.
  • 将推理任务设为一个多标签分类问题.
  • 开发了一个数字POI特征提取的统计方法.
  • 研究了模糊推理系统 (FIS),支持矢量机 (SVM) 和多层感知器 (MLP) 推理引擎.
  • 使用独立和统一的策略实施了11个推断引擎变体.

主要成果:

  • 拟议的框架在不同的推理系统中表现出良好的概括性.
  • 基于MLP的推断引擎采用统一策略,获得了最高的F1总分0.8699.
  • 性能最好的模型表现出每样本0.0002毫秒的异常快速推断时间.

结论:

  • 开发的推断框架既可通用,也有效地驱动环境识别.
  • 具有统一策略的MLP模型为自动驾驶中的实时应用提供了一个有希望的解决方案.
  • POI数据与先进的机器学习相结合,有效地提高了车辆的情境意识.