Validation of Enhanced Emotion Enabled Cognitive Agent Using Virtual Overlay Multi-Agent System Approach
Faisal Riaz, Muaz A. Niazi

TL;DR
This paper enhances and validates an emotion-inspired cognitive agent for autonomous vehicles, demonstrating improved safety features like reduced stopping and overtaking distances through simulation and practical tests.
Contribution
The study introduces a revised EEC_Agent architecture with a fear emotion mechanism validated using VOMAS within the CABC framework, advancing agent-based collision avoidance in AVs.
Findings
Enhanced EEC_Agent can feel different fear levels in traffic scenarios.
The agent requires smaller stopping and overtaking distances than human drivers.
Validation using VOMAS confirms the effectiveness of the fear mechanism.
Abstract
Making roads safer by avoiding road collisions is one of the main reasons for inventing Autonomous vehicles (AVs). In this context, designing agent-based collision avoidance components of AVs which truly represent human cognition and emotions look is a more feasible approach as agents can replace human drivers. However, to the best of our knowledge, very few human emotion and cognition-inspired agent-based studies have previously been conducted in this domain. Furthermore, these agent-based solutions have not been validated using any key validation technique. Keeping in view this lack of validation practices, we have selected state-of-the-art Emotion Enabled Cognitive Agent (EEC_Agent), which was proposed to avoid lateral collisions between semi-AVs. The architecture of EEC_Agent has been revised using Exploratory Agent Based Modeling (EABM) level of the Cognitive Agent Based Computing…
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Taxonomy
TopicsEvacuation and Crowd Dynamics · Transportation and Mobility Innovations · Traffic control and management
