Enhanced emotion enabled cognitive agent-based rear-end collision avoidance controller for autonomous vehicles. (November 2018)

• Record Type: Journal Article
• Authors: Riaz, Faisal; Niazi, Muaz A
• Is Part Of: Simulation. Volume 94:Number 11(2018)
• Page Start: 957
• Page End: 977
• Publication Date: 2018-11
• Subjects: Autonomous vehicle -- cognitive agent -- emotion -- OCC model -- rear-end collision; Computer simulation -- Periodicals; 003.3
• DOI: 10.1177/0037549717742203
• Languages: English
• ISSNs: 0037-5497

Abstract

Amongst collisions, rear-end collisions are the deadliest. Several rear-end collision avoidance solutions have been proposed recently in the literature. A key problem with existing solutions is their dependence on precise mathematical models. However, real world driving is influenced by a number of nonlinear factors. These include road surface conditions, driver reaction time, pedestrian flow, and vehicle dynamics. These factors involve so many different variations that precise mathematical solutions are hard to obtain, if not impossible. This problem with precise control-based rear-end collision avoidance schemes has also previously been addressed using fuzzy logic, but the excessive number of fuzzy rules straightforwardly prejudices their efficiency. Furthermore, such fuzzy logic-based controllers have been proposed without the use of an appropriate modeling technique. One such modeling technique is agent-based modeling. This technique is suitable because it allows for mimicking the functions of an artificial human driver executing fuzzy rules. Keeping in view these limitations, we propose an enhanced emotion enabled cognitive agent (EEEC_Agent)-based controller. The proposed EEEC_Agent helps autonomous vehicles (AVs) avoid rear-end collisions with fewer rules. One key innovation in its design is to use the human emotion of fear. The resultant agent is very efficient and also uses the Ortony–Clore–Collins (OCC) model. The fear generation mechanism of EEEC_Agent is verified through NetLogo simulation. Furthermore, practical validation of EEEC_Agent functions is performed by using a specially built prototype AV platform. Finally, a qualitative comparison with existing state-of-the-art research works reflects that the proposed model outperforms recent research proposals.