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Towards social autonomous vehicles: Efficient collision avoidance scheme using Richardson's arms race model
1Dept. Of Computing-Iqra University, Islamabad, Pakistan.
Plos One
|October 18, 2017
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.
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.
- 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.
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