Shi, S., Tang, W., Wang, Y. (2017). A Review on Fatigue Driving Detection. ITM Web of Conferences 12, 01019. https://core.ac.uk/download/pdf/192105474.pdf
In this article, Shi et al. (2017) reviewed on the detection of fatigue driving. The issue of fatigue driving becoming a hazard on the road is being addressed. There are different methods to detect fatigue driving and its degree. A method used includes the driver’s psychological aspects; like brain waves, heart rate, pulse beat and breathing regularity, but these methods require the equipment to have direct contact with the driver. An alternative method used is based on the driver’s facial features like percentage of eyelid closure (PERCLOS), the detection of the driver’s head position, gaze direction, blink frequency and yawning. The results from the facial features detection may be difficult to obtain as the recognition of the algorithm is complex. Detaching from the driver, the vehicle’s driving parameters are also one of the detection methods to derive the degree of fatigue. Detection of the vehicle’s offset (from the travel path), vehicle’s speed, steering wheel angle and its wheel grip can also help with the analysis of fatigue driving. Despite the diverse methods available, further extensive research should be carried out to provide more robust detection results. Unfortunately, due to the lack of detection accuracy and its high cost, the fatigue detection technologies only appear on selected exclusive cars. This reflects the need for less expensive but high accuracy detection system for more cars in the future.
The article also provides useful information for our research project on the part of fatigue driving. It has aided in our understanding and widened our perspective about the availability of various methods that can detect the driver’s degree of fatigue. Different methods of detection have their own pros and cons and this article has reassured our team that the different methods can work hand-in-hand to detect the driver’s degree of fatigue. When various methods are combined, like the ones in our project; steering wheel grip detection, PERCLOS and blink frequency, they can provide more information to reflect the driver’s fatigue level.
