Paper Info
Title
Towards Social Autonomous Vehicles: Efficient Collision Avoidance Scheme Using Richardson'S Arms Race Model
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.
Year
DOI
Venue
2017
10.1371/journal.pone.0186103
PLOS ONE
Field
DocType
Volume
Front and back ends,Arms race,Embedding,Collision detection,Computer science,Copying,Collision,Network topology,Human–computer interaction,Mirroring
Journal
12
Issue
ISSN
Citations 
10
1932-6203
0
PageRank 
References 
Authors
0.34
14
2
Name
Order
Citations
PageRank
Faisal Riaz1236.03
Muaz A. Niazi217724.55