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Research on quantum cognition in autonomous driving.

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Autonomous vehicles face challenges in predicting human behavior. This study shows that a Quantum-like Bayesian (QLB) model better predicts pedestrian actions than classical models, improving autonomous driving safety.

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Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Transportation Engineering

Background:

  • Human behavior in traffic is complex and often deviates from classical rational models.
  • Accurate prediction of pedestrian intention is crucial for autonomous driving safety.
  • Existing models struggle with the irrationality observed in real-world traffic scenarios.

Purpose of the Study:

  • To investigate the cognitive aspects of pedestrian behavior in autonomous driving contexts.
  • To propose and validate a novel model for intention estimation that accounts for bounded rationality.
  • To enhance the safety and reliability of autonomous driving systems through improved human behavior prediction.

Main Methods:

  • Application of quantum cognitive theory to model pedestrian decision-making.
  • Development and analysis of a Quantum-like Bayesian (QLB) model.
  • Comparative study with classical probability models and data-driven Social-LSTM.

Main Results:

  • The QLB model demonstrates superior ability to incorporate pedestrian reasonability compared to classical probability models.
  • Case analysis confirms the QLB model's consistency with actual pedestrian crossing behavior.
  • Trajectory prediction experiments show the QLB model effectively handles edge events in interactive scenes, outperforming Social-LSTM.

Conclusions:

  • The Quantum-like Bayesian (QLB) model offers a more realistic approach to understanding and predicting human traffic behavior.
  • This research provides a new framework for addressing the cognitive challenges of intention estimation in autonomous driving.
  • The findings contribute to the development of safer and more robust autonomous vehicle systems by accounting for bounded rationality.