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Accidents involving HGVs are also more common on expressways than on small streets and roads.

To address this issue, Heriot-Watt University Malaysia conducted a year-long project in collaboration with Malaysian Technical Standards Forum Bhd on how to reduce road accidents by actively monitoring HGV drivers in real time.

The result was the Advanced Vehicle Monitoring and Assistance System (AVMS) for HGVs.

In addition to monitoring the vehicle speed and mileage, the system also alerts the drivers whenever they divert from their lanes, are drowsy or drive above the speed limit.

“The most important factor to track is the behavior of the driver because, in Malaysia, most of the fatal road accidents are caused by reckless driving,” says Jaysern Pang Jia Yew, project lead for AVMS.

Julian Goh, one of the graduate students working on the project, explains that a camera is used to monitor the driver’s facial expression, and they have used a machine learning technique to train the artificial intelligence (AI) to detect changes in eye movements with an accuracy of about 92%.

Lim Chun Zhe, another graduate student on the AVMS development team, explains that a drowsy driver will tend to swerve left and right on the road.

Through the lane detection system combined with data sets such as driving speed, AVMS can detect reckless driving behavior and trigger a warning.

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