(The torii gate, Itsukushima Shrine, Hiroshima Prefecture, Japan. Photo by Jordy Meow.)
Sompo Japan Nipponkoa Insurance (Tokyo) is collaborating with Accenture (Dublin) and transportation company Daiichi Kotsu Sangyo to build a deep learning algorithm using the Intel IoT Platform Reference Architecture to better understand individual driving habits and identify new ways to transform driver safety within Japan’s transportation industry.
Takuya Kudo, Data Science Center of Excellence global lead and Japan lead for Accenture Analytics.
The deep learning algorithm could enable transportation companies to provide personalized safety instructions for drivers, helping reduce the number of accidents, inform the development of optimal driver rosters, and enhance training programs, according to an Accenture statement.
Accenture reports that Sompo Japan Nipponkoa Insurance will collect data from connected devices installed in Daiichi Kotsu Sangyo’s taxis. In addition to cameras capturing images and telemetry tools recording journey data, the insurer will collect biometric information, such as heart rates, from consenting taxi drivers through wearable devices.
Using Intel IoT Platform Reference Architecture
As part of an ongoing strategic relationship between Accenture and Intel, the data will be processed securely and anonymously using the Intel IoT Platform Reference Architecture that includes Intel processor-based servers equipped with Intel’s high-performance Xeon processors, Intel Gateway for data collection, and edge computing image processing technology, Accenture reports. The data will then be uploaded to the cloud for secure storage and analytics processing.
Data flow overview. Source: Accenture. (Click to enlarge.)
Accenture says it will use the input to develop an algorithm that will automatically assess the accident risk for each driver by collating and analyzing images, biometrics, and vehicle data indicating speed and driving behavior. The company identifies “deep learning”—one of the emerging advanced analytics techniques available today—as integral to the data platform.
The partners conducted an initial proof of concept in March 2017 that used data collected from 100 taxis and 100 drivers. Through the experiment, the deep-learning algorithm created intelligence that identified signs of drivers’ drowsiness and near-miss accidents from their heart-rate changes and driving behavior, Accenture reports.
“Rapid advances in IoT and autonomous driving technologies are bringing new challenges that can only be addressed by using new technologies such as this deep learning algorithm,” comments Takuya Kudo, Data Science Center of Excellence global lead and Japan lead for Accenture Analytics, part of Accenture Digital.
As part of the collaboration, Accenture says it will continue to create new intelligence by applying the latest analytics technologies to address industry challenges. For example, the ability to analyze images on a large, commercial scale is still being developed, and as part of this project Accenture reports that it is applying the latest innovations in advanced analytics and data science tools to enable it.
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