A validated fuzzy logic inspired driver distraction evaluation system for road safety using artificial human driver emotion

Authored by Faisal Riaz, Sania Khadim, Rabia Rauf, Mudassar Ahmad, Sohail Jabbar, Junaid Chaudhry

Date Published: 2018

DOI: 10.1016/j.comnet.2018.06.007

Sponsors: No sponsors listed

Platforms: NetLogo

Model Documentation: Other Narrative Flow charts Pseudocode

Model Code URLs: Model code not found

Abstract

This research paper presents a validated emotion enabled cognitive driver assistance model (EECDAM) as an accident prevention scheme while keeping in mind different types of driver distractions. It is observed that distracted drivers know that distraction can lead them to a crash but they are not aware of distractions when they take over and they continue to drive. With advancements in autonomous vehicles technologies, it is possible to have an onboard driver assistance systems. However, research is yet to be reported on this issue whether onboard driver assistance program will be effective or not. The Emotion Enabled Cognitive Driver Assistance Model is a system based on an encapsulated Emotion Enabled Cognitive Driver Assistant (EECDA), which computes the effects of external factors at the distraction level of the subject and generates algorithmically generated fear emotion. During experiments, the EECDA intervenes when the fear intensity of the driver crosses a threshold by sending two sound alerts to the driver to take appropriate action. To demonstrate the effectiveness of the proposed approach as a road safety system, a Cognitive Agent-Based Computing (CABC) framework has been utilized to validate the results of the EECDAM. Algorithms are utilized using fuzzy sets to compute distraction of the drivers. We also present an Agent-Based Model (ABM) to validate the implementation of the proposed scheme. Extensive experiments demonstrate the proficiency of the proposed model for robust collision avoidance. (C) 2018 Published by Elsevier B.V.

Tags

Model Fuzzy logic time Crash Vehicles Tracking Behaviors Assistance Driver distraction learning Artificial emotions Safe road traffic system Driver assistant model Driving performance Emergency braking Violations