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Related Experiment Videos

Towards social autonomous vehicles: Efficient collision avoidance scheme using Richardson's arms race model.

Faisal Riaz1, Muaz A Niazi2

  • 1Dept. Of Computing-Iqra University, Islamabad, Pakistan.

Plos One
|October 18, 2017
PubMed
Summary

This study introduces social autonomous agents for vehicles, enhancing safety through human-like interaction and intention prediction. This advanced collision avoidance strategy significantly outperforms existing methods in simulations and practical tests.

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Area of Science:

  • Artificial Intelligence
  • Robotics
  • Computer Science

Background:

  • Autonomous Vehicles (AVs) require sophisticated collision avoidance strategies.
  • Current methods often lack the nuanced interaction capabilities seen in human drivers.
  • The need for human-like social interaction in AVs is crucial for safe integration.

Purpose of the Study:

  • To propose a novel social autonomous agent for AVs.
  • To enable AVs to interact using social manners, including intention prediction (mentalizing) and action copying (mirroring).
  • To enhance collision avoidance efficiency and safety in AVs.

Main Methods:

  • Utilized Exploratory Agent Based Modeling (EABM) within the Cognitive Agent Based Computing (CABC) framework.
  • Developed a tailored mathematical model based on Richardson's arms race for mentalizing and mirroring.

Related Experiment Videos

  • Validated the social agent through NetLogo simulations and prototype AV experiments.

    • Main Results:

    • The social agent-based collision avoidance strategy demonstrated 78.52% greater efficiency than Random Walk in congested, flock-like topologies.
    • Practical tests showed 99.876% efficiency in avoiding rear-end and lateral collisions.
    • Outperformed existing IEEE 802.11n-based mirroring neuron schemes.

    Conclusions:

    • The proposed social autonomous agent offers a highly effective approach to AV collision avoidance.
    • Social interaction capabilities significantly improve safety compared to traditional methods.
    • This framework paves the way for more human-like and safer autonomous driving.