Research Question/Objective: Current advanced driver assistant systems combine the strengths of a human driver with the benefits of technical advancements. By raising the vehicle automation level, new human factors challenges emerge. Considering level 2 automation, where the driver is required to continuously monitor the system and remains responsible for vehicle safety, automation effects like...

Number of accidents during driving is increasing day by day and drowsy driving has been implicated as a causal factor in many accidents. Goal of driver drowsiness detection systems is to reduce these accidents. It has been seen that most of the accidents occur due to driver’s fatigue and a small due to inattention factor, therefore this paper reviews driver’s fatigue monitoring techniques in de...

In September 2008 new regulations for managing heavy vehicle driver fatigue entered into force in Australia. According to the new regulations there is a chain of responsibility ranging from drivers to dispatchers and shippers and thus, carriers must explicitly consider driving and working hour regulations when generating truck driver schedules. This paper presents and studies the Australian Tru...

Driver fatigue is an important factor in traffic accidents, and the development of a detection system for driver fatigue is of great significance. To estimate and prevent driver fatigue, various classifiers based on electroencephalogram (EEG) signals have been developed; however, as EEG signals have inherent non-stationary characteristics, their detection performance is often deteriorated by ba...

There is no doubt that fatigue plays an important role in driver performance and potential crashes. One way to understand the effects of fatigue on driving performance that has not been rigorously explored is to use cognitive modeling as a performance predictor. In this paper, we integrate existing models of driving and fatigue to make a general model of driving under the influence of moderate ...

This paper proposes a real-time electroencephalogram (EEG)-based detection method of the potential danger during fatigue driving. To determine driver fatigue in real time, wavelet entropy with a sliding window and pulse coupled neural network (PCNN) were used to process the EEG signals in the visual area (the main information input route). To detect the fatigue danger, the neural mechanism of d...

Modern vehicles are designed to protect occupants in the event of a crash. However, passenger protection can be combined with collision avoidance. Statistics have shown that human error is the number one contributor to road accidents. Advanced driver behavior and monitoring systems have been developed by manufacturers in recent years and many have been proven to be effective systems in the prev...

In this paper, in order to implement a computer vision-based recognition system of driving fatigue. In addition to detecting human face in different light sources and the background conditions, and tracking eyes state combined with fuzzy logic to determine whether the driver of the physiological phenomenon of fatigue from face of detection. Driving fatigue recognition has been valued highly in ...

Driver fatigue has become one of the major causes of traffic accidents, and is a complicated physiological process. However, there is no effective method to detect driving fatigue. Electroencephalography (EEG) signals are complex, unstable, and non-linear; non-linear analysis methods, such as entropy, maybe more appropriate. This study evaluates a combined entropy-based processing method of EEG...