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Related Concept Videos

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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PD Controller: Design01:26

PD Controller: Design

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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.
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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.
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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.
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Feedback control systems01:26

Feedback control systems

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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
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Open and closed-loop control systems01:17

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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.
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Driver Characteristics Oriented Autonomous Longitudinal Driving System in Car-Following Situation.

Haksu Kim1, Kyunghan Min2, Myoungho Sunwoo1

  • 1Department of Automotive Engineering, Hanyang University, Seoul 04763, Korea.

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Summary

This study introduces a personalized driving system that mimics individual driver behavior for adaptive cruise control, enhancing comfort and safety in car-following situations. The system ensures harmony between automated control and driver intention.

Keywords:
autonomous longitudinal drivingelectric vehicle controlindividual driver behavior modelingpersonalized speed planning

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Area of Science:

  • Automotive Engineering
  • Human-Computer Interaction
  • Control Systems

Background:

  • Advanced driver assistance systems (ADAS) aim to reduce driving burden and enhance comfort.
  • Current ADAS often lack personalization, leading to driver discomfort (heterogeneity).
  • A need exists for autonomous systems that align with individual driving intentions.

Purpose of the Study:

  • To propose a personalized longitudinal driving system for car-following scenarios.
  • To develop a system that mimics individual driving behavior for improved driver acceptance.
  • To ensure harmony between automated control and driver intent.

Main Methods:

  • A multi-layer framework comprising a speed planner and a driver parameter manager was developed.
  • The speed planner uses a parametric cost function and constraints reflecting driver characteristics.
  • Driver parameters are identified from real driving data via the driver parameter manager.

Main Results:

  • The proposed system successfully mimics the driving style of actual drivers.
  • Driving simulation validated the algorithm's ability to personalize longitudinal control.
  • The system maintained safety by preventing collisions with preceding vehicles.

Conclusions:

  • Personalized speed planning algorithms can effectively address heterogeneity in ADAS.
  • Mimicking individual driving behavior enhances driver comfort and system acceptance.
  • The developed system offers a promising approach for personalized autonomous driving.