  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 (CABC) framework
and real-time fear emotion generation mechanism using the Ortony, Clore &
Collins (OCC) model has also been introduced. Then the proposed fear generation
mechanism has been validated using the Validated Agent Based Modeling level of
CABC framework using a Virtual Overlay MultiAgent System (VOMAS). Extensive
simulation and practical experiments demonstrate that the Enhanced EEC_Agent
exhibits the capability to feel different levels of fear, according to
different traffic situations and also needs a smaller Stopping Sight Distance
(SSD) and Overtaking Sight Distance (OSD) as compared to human drivers.

Multiagent Systems;Artificial Intelligence;Computer Science and Game Theory;Systems and Control