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Related Concept Videos

Decision Making01:20

Decision Making

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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.
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Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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Vehicle-to-everything decision optimization and cloud control based on deep reinforcement learning.

Zhenhai Gao1,2, Dayu Liu2, Chengyuan Zheng3

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

Scientific Reports
|August 9, 2025
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Summary
This summary is machine-generated.

This study introduces a Vehicle-to-Everything (V2X) framework for autonomous driving, enhancing decision optimization and hazard assessment. The V2X system significantly improves driving accuracy and reduces response times for safer, more efficient autonomous navigation.

Keywords:
Autonomous drivingDecision optimizationDeep reinforcement learningHazard classificationVehicle-to-everything

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Area of Science:

  • Intelligent Transportation Systems
  • Autonomous Driving Technology
  • Machine Learning in Automotive Applications

Background:

  • Complex traffic environments pose significant challenges for autonomous driving systems, particularly in decision optimization and real-time hazard assessment.
  • Existing autonomous driving frameworks often struggle with integrating diverse data sources and ensuring rapid, accurate decision-making.
  • Enhancing vehicle safety and responsiveness requires advanced V2X communication and intelligent control strategies.

Purpose of the Study:

  • To propose a novel Vehicle-to-Everything (V2X) decision framework to improve autonomous driving safety and responsiveness.
  • To develop integrated modules for vehicle perception, decision-making, and road segment hazard classification.
  • To design an autonomous driving cloud control platform for enhanced decision optimization and data analysis.

Main Methods:

  • Implemented a V2X framework with three modules: vehicle perception (sensor fusion, deep neural networks), decision-making (deep reinforcement learning), and execution.
  • Developed a road segment hazard classification module using historical and real-time data with a hazard evaluation model.
  • Designed a cloud control platform for centralized computing, large-scale data analysis, and collaborative optimization.

Main Results:

  • The V2X decision optimization method improved vehicle decision accuracy from 89.2% to 98.2% (a 9.0% increase).
  • The cloud control system's response time decreased by 28.7% (from 178 ms to 127 ms), enhancing real-time performance.
  • The hazard classification model achieved 99.5% accuracy, maintaining over 95% in complex traffic conditions.

Conclusions:

  • The proposed V2X decision optimization framework and cloud control platform significantly enhance the decision quality and safety of autonomous driving systems.
  • The integrated approach effectively addresses challenges in complex traffic environments through improved perception, optimized decision-making, and accurate hazard assessment.
  • Experimental results validate the framework's effectiveness, demonstrating substantial improvements in accuracy, response time, and adaptability.