Paper Info
Title
Fuzzy-based Driver Monitoring System (FDMS): Implementation of two intelligent FDMSs and a testbed for safe driving in VANETs.
Abstract
Vehicular Ad hoc Networks (VANETs) have gained a great attention due to the rapid development of mobile Internet and Internet of Things (IoT) applications. On the other hand, the competition in the automotive industry has turned into an unprecedented race to who will be the first to provide the fully autonomous cars. However, the fully autonomous driving is still a bit far from deployment, and for now, they are providing automation only at a certain level and, at the same time, are offering connected services through their mobility service platforms. With Fog and Edge computing integrated in VANETs, these mobility platforms will be standardized to provide services for every car on the road, which will help VANETs to accomplish one of its main goals, the road safety. In this paper, we propose an intelligent Fuzzy-based Driver Monitoring System (FDMS) for safe driving. We present and compare two fuzzy-based systems: FDMS1 and FDMS2. To make a decision, FDMS1 considers Vehicle’s Environment Temperature (VET), Noise Level (NL) and Heart Rate (HR). While, for FDMS2, we consider Respiratory Rate (RR) as a new parameter to decide Driver’s Situational Awareness (DSA). We evaluate the ability of the driver to safely operate the vehicle by monitoring his condition and subsequently, based on the system output, a smart box informs the driver and provides assistance. We show through simulations and experiments the effect of the considered parameters on the determination of the driver’s situation and demonstrate the actions that can be performed accordingly.
Year
DOI
Venue
2020
10.1016/j.future.2019.12.030
Future Generation Computer Systems
Keywords
Field
DocType
VANETs,IoT,Fog computing,Edge computing,Fuzzy logic,DMS
Edge computing,Software deployment,Computer science,Situation awareness,Fuzzy logic,Testbed,Real-time computing,Automation,Automotive industry,The Internet
Journal
Volume
ISSN
Citations 
105
0167-739X
1
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Kevin Bylykbashi11415.87
Ermioni Qafzezi228.13
Makoto Ikeda31202207.10
Keita Matsuo421255.01
Leonard Barolli52178333.62