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相关概念视频

Decision Making01:20

Decision Making

101
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...
101
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

105
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
105
PD Controller: Design01:26

PD Controller: Design

208
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,...
208
Controller Configurations01:22

Controller Configurations

90
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...
90
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

86
Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
86
Root-Locus Method01:19

Root-Locus Method

141
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...
141

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端到端自动驾驶决策方法基于复杂场景中改进的TD3算法.

Tao Xu1, Zhiwei Meng1, Weike Lu2

  • 1National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun 130015, China.

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概括

这项研究通过解决目标网络低估问题,改善了自动驾驶的强化学习. 改进的算法在复杂的驾驶场景中显示了更快的融合和更好的稳定性.

关键词:
自动驾驶自动驾驶的自动驾驶.复杂的场景 复杂的场景智能决策 - 智能决策 - 智能决策多个批评家多个批评家.强化学习是一种强化学习.

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科学领域:

  • 智能汽车系统 智能汽车系统
  • 机器学习 机器学习
  • 强化学习是一种强化学习.

背景情况:

  • 智能汽车系统需要在复杂场景中做出强有力的决策.
  • 传统的方法难以应对复杂的驾驶环境.
  • 强化学习提供了更高的决策能力,但面临着估计的不准确性.

研究的目的:

  • 解决自动驾驶强化学习的低估现象.
  • 提出一个端到端的决策方法,克服TD3算法的局限性.
  • 提高自动驾驶政策的准确性和稳定性.

主要方法:

  • 开发了一种改进的双延迟深度决定性政策梯度 (TD3) 算法.
  • 引入了目标最大化的三重关键结构,以解决低估问题.
  • 为了稳定政策,采用了多个时间级平均值.

主要成果:

  • 提出的方法有效地解决了低估问题.
  • 与基线方法相比,该算法显示出更高的收速度.
  • 在模拟中实现了更好的估计准确性和政策稳定性.

结论:

  • 增强的TD3算法为自动驾驶提供了更准确,更稳定的决策框架.
  • 该方法显示了现实世界智能汽车应用的巨大潜力.
  • 三重关键结构和多步平均是TD3限制的有效解决方案.