Road collisions and casualties pose a serious threat to commuters around the globe. Autonomous Vehicles (AVs) aim to make the use of technology to reduce the road accidents. However, the most of research work in the context of collision avoidance has been performed to address, separately, the rear end, front end and lateral collisions in less congested and with high intervehicular distances. Whereas the flock like topology, a typical scenario of urban traffic single one-way lane, where the traffic pattern is congested, the inter-vehicular distance is small and the chances of collisions is very high, has not been addressed sufficiently. The collision avoidance capabilities of AVs have been improved by using different methodologies, however, humaninspired designs have not been explored in this context, especially the human brain parts that involve in human-human interaction that make them social and help them in understanding and adapting the behaviour of other humans. The goal of this paper is to introduce the concept of a social agent, which interact with other AVs in social manners like humans are social having the capability of predicting intentions, i.e. mentalizing and copying the actions of each other, i.e. mirroring. The proposed social agent is 2 based on a human-brain inspired mentalizing and mirroring capabilities and has been modelled for collision detection and avoidance under congested urban road traffic. A detailed literature review has been performed to find out the existing research that uses human social life techniques to avoid the collisions between vehicles. During the literature review, Richardson's arms race model has been found very near to our problem that can be applied in this paper after modifications to the AVs to have efficient collision avoidance capabilities in a flock like topology. Then, we designed our social agent having the capabilities of mentalizing and mirroring and for this purpose we utilized Exploratory Agent Based Modeling (EABM) level of Cognitive Agent Based Computing (CABC) framework proposed by Niazi and Hussain (1). In the next step, we modified the differential equations defined by Richardson's arms race model to emulate the concept of mentalizing and mirroring in the proposed social agent. Then, the simulation model has been designed that helps in testing the behaviour of AVs in terms of collision avoidance using the proposed social agent based model and random walk. Then, using the behaviour space tool of NetLogo simulator, the performance of the proposed social agent based scheme has been computed and its results have been compared with the random walk based collision avoidance scheme. Furthermore, the practical validation of the proposed agent has been performed by building the proposed social agent inspired prototype AV. For the practical validation, a real road flock like topology has been created with the help of human driven motorcycles. In the last, the performance of proposed research work has been compared with the existing state-of-the-art. 3 Our simulation and practical experiments reveal that by embedding Richardson's arms race model within AVs, collisions can be avoided while travelling on congested urban roads in a flock like topologies. The performance of the proposed social agent has been compared at two different levels. First at simulation level, the performance of the proposed social agent is compared using extensive experiment sets with Random walk based collision avoidance strategy and it has been found that proposed social agent based collision avoidance strategy is 78.52 % efficient than Random walk based collision avoidance strategy in a congested flock like topologies. Then the practical validation is performed in terms of efficiency and the results confirmed that the proposed scheme can avoid rear end and lateral collisions with the efficiency of 99.876 % as compared to the IEEE 802.11n based existing state of the art (33) mirroring neuron based collision avoidance scheme.
Riaz et al. REF utilized an ABM to model collision detection and avoidance under congested urban road traffic.
Towards Social Autonomous Vehicles: Efficient Collision Avoidance Scheme Using Richardson's Arms Race Model
PLoS ONE12(10): e0186103 (2017)
Computer Science Medicine