Collaboration uses deep learning algo to improve driver safety in Japan

Collaboration uses deep learning algo to improve driver safety in Japan

Sompo Japan Nipponkoa Insurance, Daiichi Kotsu Sangyo and Accenture are collaborating to build a deep learning algorithm to better understand individual driving habits and identify new ways to transform driver safety within Japan’s transportation industry.

The new algo 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.

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, biometric information such as heart rates will be collected from consenting taxi drivers through wearable devices.

Accenture 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. Deep learning, which is one of the emerging advanced analytics techniques available today, will be integral to the data platform.

“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,” said Takuya Kudo, Data Science Center of Excellence global lead and Japan lead for Accenture Analytics, part of Accenture Digital.

In an initial Proof of Concept experiment conducted in March 2017 that used data collected from 100 taxis and 100 drivers, the deep-learning algorithm created intelligence that identified signs of drivers’ drowsiness and near-miss accidents from their heart-rate changes and driving behavior.

Sursa: Fintech Innovation

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