Abstract
This paper presents the concept of a social autonomous agent to conceptualize such Autonomous Vehicles (AVs), which interacts with other AVs using social manners similar to human behavior. The presented AVs also have the capability of predicting intentions, i.e. mentalizing and copying the actions of each other, i.e. mirroring. Exploratory Agent Based Modeling (EABM) level of the Cognitive Agent Based Computing (CABC) framework has been utilized to design the proposed social agent. Furthermore, to emulate the functionality of mentalizing and mirroring modules of proposed social agent, a tailored mathematical model of the Richardson’s arms race model has also been presented. The performance of the proposed social agent has been validated at two levels–firstly it has been simulated using NetLogo, a standard agent-based modeling tool and also, at a practical level using a prototype AV. The simulation results have confirmed that the proposed social agent-based collision avoidance strategy is 78.52% more efficient than Random walk based collision avoidance strategy in congested flock-like topologies. Whereas practical results have confirmed that the proposed scheme can avoid rear end and lateral collisions with the efficiency of 99.876% as compared with the IEEE 802.11n-based existing state of the art mirroring neuron-based collision avoidance scheme.
Highlights
Road collisions are an inevitable element of human life
If we study the results of the first test it can be seen that social Autonomous Vehicles (AVs) takes 0.00138 seconds to sense the three neighbouring vehicles and found the front vehicle at the distance of 2.6 ft, and Lateral Left (LL) and Lateral Right (LR) vehicles in 1.8 and 3.2 ft respectively
Artificial intelligence is the name of building machines, which act like human beings, by studying human beings
Summary
Road collisions are an inevitable element of human life. Riaz and Niazi [1] have presented literature showing that road collisions have the potential of becoming the 5th major cause of human deaths by the 2030. According to Libero et al [12], the ability to interpret agents’ intent of their actions is a vital skill in a successful social interaction and can be explored to enhance the pre-crash sensing and avoidance capabilities of AVs by making quick decisions in short reaction time. This line of research has not been explored in the case of AVs that help them to be social and understanding the dangerous intents of other AVs and to avoid collisions.
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