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Background Road collisions and casualties pose a serious threat to commutersaround the globe. Autonomous Vehicles (AVs) aim to make the use of technologyto reduce the road accidents. However, the most of research work in the contextof collision avoidance has been performed to address, separately, the rear end,front end and lateral collisions in less congested and with highinter-vehicular distances. Purpose The goal of this paper is to introduce theconcept of a social agent, which interact with other AVs in social manners likehumans are social having the capability of predicting intentions, i.e.mentalizing and copying the actions of each other, i.e. mirroring. The proposedsocial agent is based on a human-brain inspired mentalizing and mirroringcapabilities and has been modelled for collision detection and avoidance undercongested urban road traffic.  Method We designed our social agent having the capabilities of mentalizingand mirroring and for this purpose we utilized Exploratory Agent Based Modeling(EABM) level of Cognitive Agent Based Computing (CABC) framework proposed byNiazi and Hussain.  Results Our simulation and practical experiments reveal that by embeddingRichardson's arms race model within AVs, collisions can be avoided whiletravelling on congested urban roads in a flock like topologies. The performanceof the proposed social agent has been compared at two different levels.

Towards Social Autonomous Vehicles: Efficient Collision Avoidance Scheme Using Richardson's Arms Race Model