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Making roads safer by avoiding road collisions is one of the main reasons forinventing Autonomous vehicles (AVs). In this context, designing agent-basedcollision avoidance components of AVs which truly represent human cognition andemotions look is a more feasible approach as agents can replace human drivers.However, to the best of our knowledge, very few human emotion andcognition-inspired agent-based studies have previously been conducted in thisdomain. Furthermore, these agent-based solutions have not been validated usingany key validation technique. Keeping in view this lack of validationpractices, 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 BasedModeling (EABM) level of the Cognitive Agent Based Computing (CABC) frameworkand real-time fear emotion generation mechanism using the Ortony, Clore &Collins (OCC) model has also been introduced. Then the proposed fear generationmechanism has been validated using the Validated Agent Based Modeling level ofCABC framework using a Virtual Overlay MultiAgent System (VOMAS). Extensivesimulation and practical experiments demonstrate that the Enhanced EEC_Agentexhibits the capability to feel different levels of fear, according todifferent traffic situations and also needs a smaller Stopping Sight Distance(SSD) and Overtaking Sight Distance (OSD) as compared to human drivers.

Validation of Enhanced Emotion Enabled Cognitive Agent Using Virtual Overlay Multi-Agent System Approach