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Related Concept Videos

Decision Making01:20

Decision Making

172
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...
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Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
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Reason and Intuition01:37

Reason and Intuition

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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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Decision Making: P-value Method01:09

Decision Making: P-value Method

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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

137
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...
137
Control Systems: Applications01:25

Control Systems: Applications

687
Electrical engineering plays a pivotal role in our daily lives, with control systems at the heart of many applications, from home appliances to sophisticated space shuttles. Control systems manage and regulate the behavior of devices and processes, ensuring they function safely, correctly, and efficiently.
In modern vehicles, control systems manage various functions to enhance performance and safety. The steering wheel and accelerator are primary inputs in a car's control system. The...
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A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
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How Do Autonomous Vehicles Decide?

Sumbal Malik1,2, Manzoor Ahmed Khan1,2, Hesham El-Sayed1,2

  • 1College of Information Technology, United Arab Emirates University, Abu Dhabi 15551, United Arab Emirates.

Sensors (Basel, Switzerland)
|January 8, 2023
PubMed
Summary
This summary is machine-generated.

This paper analyzes decision-making solutions for autonomous driving, highlighting challenges in complex urban environments. It identifies research gaps to advance higher-level autonomous vehicle safety and efficiency.

Keywords:
autonomous drivingbehavioural planningdecision-making

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Area of Science:

  • Robotics and Artificial Intelligence
  • Transportation Engineering
  • Computer Science

Background:

  • Autonomous driving technologies aim to improve road safety and efficiency, reducing accidents caused by human error.
  • High-level autonomous systems require robust decision-making capabilities to navigate dynamic and uncertain environments.
  • Current research extensively covers autonomous vehicle deployment but less so high-level decision-making in complex urban settings.

Purpose of the Study:

  • To analyze and categorize existing decision-making approaches for autonomous driving systems.
  • To compare these approaches with classical decision-making methods.
  • To identify critical research gaps and open challenges in autonomous driving decision-making.

Main Methods:

  • Literature review and analysis of various decision-making solution categories for autonomous vehicles.
  • Comparative analysis against traditional decision-making frameworks.
  • Identification and discussion of research gaps and future challenges.

Main Results:

  • Categorization of diverse decision-making approaches for autonomous driving.
  • Comparison of modern techniques with classical methods.
  • Highlighting of significant research gaps in handling complex, uncertain urban driving scenarios.

Conclusions:

  • Advanced sensor, network, and AI technologies are crucial for autonomous driving.
  • Robust decision-making is key to intelligent vehicles, especially in complex urban environments.
  • Addressing identified research gaps is essential for the safe and efficient deployment of higher-level autonomous vehicles.