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相关概念视频

Root-Locus Method01:19

Root-Locus Method

185
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...
185
Open and closed-loop control systems01:17

Open and closed-loop control systems

828
Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal...
828
Feedback control systems01:26

Feedback control systems

352
Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
352
Controller Configurations01:22

Controller Configurations

128
Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
Control-system compensation involves various configurations, most commonly series or cascade compensation, in which the controller...
128
PD Controller: Design01:26

PD Controller: Design

293
In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
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Hierarchy of Motor Control01:18

Hierarchy of Motor Control

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The hierarchy of motor control refers to the different levels of organization and processing involved in controlling movement in the body. These levels range from higher cortical areas involved in planning and decision-making to lower spinal cord reflexes that respond automatically to external stimuli.
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相关实验视频

Updated: Jul 27, 2025

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
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在线个性化偏好学习方法基于对车道中心控制轨迹的信息查询.

Wei Ran1, Hui Chen1, Taokai Xia1

  • 1School of Automotive Studies, Tongji University, Shanghai 201804, China.

Sensors (Basel, Switzerland)
|June 10, 2023
PubMed
概括

本研究介绍了自动驾驶汽车的在线个性化偏好学习方法 (OPPLM). 它准确地学习个体驾驶员对驾驶风格的偏好,使用最少的查询,增强个性化的自动驾驶体验.

关键词:
贝叶斯的方法是贝叶斯的方法.在LCC的轨迹.在线学习在线学习.偏好学习学习学习实用性理论是一种实用性理论.

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科学领域:

  • 人与计算机的交互
  • 机器人技术 机器人技术 机器人技术
  • 人工智能的人工智能

背景情况:

  • 自动驾驶汽车 (AV) 和先进驾驶辅助系统 (ADAS) 的个性化通常假设司机希望车辆模仿自己的驾驶.
  • 这种假设可能并不普遍,因为个人的驾驶偏好有很大差异.
  • 现有的方法缺乏强大的方法来学习这些独特的,个性化的驾驶偏好.

研究的目的:

  • 提出一个在线个性化偏好学习方法 (OPPLM),准确地学习个人驾驶员对AV的偏好.
  • 为了解决当前系统的局限性,这些系统假定一个适合所有驾驶风格的单一尺寸.
  • 开发一个适应每个驾驶员独特轨迹偏好的系统.

主要方法:

  • 使用对对比较组偏好查询和贝叶斯式学习方法.
  • 采用基于实用理论的两层层次层次结构模型来表示驾驶员的偏好.
  • 模拟驾驶员查询响应中的不确定性,并使用信息和贪的查询选择以提高效率.
  • 提出了一个收标准,以确定何时找到驾驶员喜欢的轨迹.

主要成果:

  • OPPLM 显示了快速的趋同,在用户研究中平均只需要 11 个查询.
  • 在车道中心控制 (LCC) 系统的背景下,成功地学习了驾驶员喜欢的轨迹.
  • 来自驾驶员偏好模型的估计效用与受试者评估得分有很高的一致性.

结论:

  • 拟议的OPPLM有效和高效地学习自动驾驶汽车的个性化驾驶偏好.
  • 这种方法克服了模仿驾驶员方法的局限性,通过适应驾驶员的个人需求.
  • 这些发现支持开发更复杂,以用户为中心的自动驾驶系统.