Paper Info
Title
SmartBlackBox: Enhancing Driver's Safety Via Real-Time Machine Learning on IoT Insurance Black-Boxes
Abstract
The Internet of Things (IoT) is shifting from a purely technical perspective to being a technology with implications on society and its economy. Responding to the global rise of awareness on how the IoT impacts on important themes such as health, safety and social responsibility, we propose and evaluate an IoT device in a field where safety is critical: a smart black-box for the automotive sector, starting from the concept popularized by insurance companies. The SmartBlackBox is a device that supports on-board machine learning to classify the drivers' behavior and supply valuable insight on how to enhance their driving styles. It features reconfigurable hardware within an embedded System-on-a-Chip that is programmed to transform what is usually a simple IoT data-ingestion node into an intelligent companion that learns the drivers' behavior, supporting them in achieving a safer driving style. Compared to traditional black-boxes, thanks to accelerators synthesized on reconfigurable hardware, the SmartBlackBox enters the domain of cyber-physical systems as it supports faster data input streams coming from multiple sensors, ad-hoc data compression for edge-cloud communication, and, especially, realtime classification of driving maneuvers.
Year
DOI
Venue
2020
10.1109/GCAIoT51063.2020.9345888
2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)
Keywords
DocType
ISBN
Advanced Driver Assistance Systems,Internet of Things,Reconfigurable Hardware,Random Forest,Safety
Conference
978-1-7281-8421-0
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Eliana S. Stivan100.34
Andrea Damiani200.68
Emanuele Del Sozzo3409.12
Marco D. Santambrogio477191.15