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相关概念视频

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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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 of...
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Evaluating Limits by Direct Substitution01:29

Evaluating Limits by Direct Substitution

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In the analysis of functions that represent continuous physical phenomena, it is often necessary to determine the output value as the input approaches a specific point. When a combination of algebraic terms defines the function and exhibits no discontinuities or abrupt changes near the point of interest, the limit of the function can be evaluated directly. This process, known as direct substitution, involves replacing the variable in the expression with the value it approaches.Direct...
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Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
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Controller Configurations01:22

Controller Configurations

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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...
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Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

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Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
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Root-Locus Method01:19

Root-Locus Method

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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...
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相关实验视频

Updated: Jan 16, 2026

Driving Simulation in the Clinic: Testing Visual Exploratory Behavior in Daily Life Activities in Patients with Visual Field Defects
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在逻辑场景参数空间中全覆盖自动驾驶系统的测试方法

Haitao Min1, Zhiqiang Zhang1,2, Tianxin Fan1

  • 1National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun 130025, China.

Sensors (Basel, Switzerland)
|September 27, 2025
PubMed
概括

本研究引入了一种测试自动驾驶系统 (ADS) 的新方法,通过确保完全覆盖逻辑场景. 混合算法有效地选择参数,提高安全性和测试可重复性.

关键词:
自动驾驶系统自动驾驶系统具体的场景代表性 具体的场景代表性全面覆盖测试的测试覆盖范围.测试场景测试场景

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科学领域:

  • 工程 工程师 工程师 工程师
  • 计算机科学 计算机科学
  • 人工智能的人工智能

背景情况:

  • 基于场景的测试对于自动驾驶系统 (ADS) 的安全至关重要.
  • 由于参数空间的复杂性和系统的可变性,当前的方法在公平性和覆盖率方面扎.

研究的目的:

  • 开发一种自动驾驶系统测试方法,以全面覆盖逻辑场景.
  • 提出一种量化方法来评估场景的代表性.
  • 为选择代表性参数创建一个高效的算法.

主要方法:

  • 使用自然驾驶数据和危险分析,对场景代表性的定量评估.
  • 一种混合算法,结合了热导向的层次搜索和参数选择的遗传优化.
  • 通过对主要车辆制动和切入场景的实证研究进行验证.

主要成果:

  • 实现了逻辑场景参数空间的100%覆盖率,边界拟合误差为8%.
  • 在覆盖率和错误率方面表现优于蒙特卡洛 (84.3%),组合测试 (86.5%) 和重要性抽样 (72.0%).
  • 证明了对代表性参数的有效选择,以全面覆盖逻辑场景.

结论:

  • 拟议的方法确保了逻辑场景空间的全面覆盖,具体场景较少.
  • 这种方法提高了ADS测试场景生成的一致性,可重现性和效率.
  • 该方法有效地支持开发自动驾驶系统的可靠安全评估框架.