Abstract
The anticipatory recognition of braking is essential to prevent traffic accidents. For instance, driving assistance systems can be useful to properly respond to emergency braking situations. Moreover, the response time to emergency braking situations can be affected and even increased by different driver's cognitive states caused by stress, fatigue, and extra workload. This work investigates the detection of emergency braking from driver's electroencephalographic (EEG) signals that precede the brake pedal actuation. Bioelectrical signals were recorded while participants were driving in a car simulator while avoiding potential collisions by performing emergency braking. In addition, participants were subjected to stress, workload, and fatigue. EEG signals were classified using support vector machines (SVM) and convolutional neural networks (CNN) in order to discriminate between braking intention and normal driving. Results showed significant recognition of emergency braking intention which was on average 71.1% for SVM and 71.8% CNN. In addition, the classification accuracy for the best participant was 80.1 and 88.1% for SVM and CNN, respectively. These results show the feasibility of incorporating recognizable driver's bioelectrical responses into advanced driver-assistance systems to carry out early detection of emergency braking situations which could be useful to reduce car accidents.
Highlights
According to the World Health Organization (WHO), every year traffic accidents cause the death of 1.3 million people around the world, about 50 million people suffer from a disability caused by accidents related to cars (WHO, 2011)
Notice that this work considers the early detection of braking intention form EEG irrespective of the cognitive state(s) experienced by the drivers
It is not possible to compare these results with previous studies due to those related works were performed in different experimental settings without contemplating the potential effect on brain processes of different driver’s cognitive states cause by fatigue, stress, workload during driving a car
Summary
According to the World Health Organization (WHO), every year traffic accidents cause the death of 1.3 million people around the world, about 50 million people suffer from a disability caused by accidents related to cars (WHO, 2011). Among the principal causes of the high car-related accidents and mortality are human errors (Subramanian, 2007) which are largely correlated to distractions, tiredness, or the simultaneous realization of other activities during driving (Allnutt, 1987; Horowitz and Dingus, 1992; Summala and Mikkola, 1994; Petridou and Moustaki, 2000) To mitigate this problem, driving assistance systems appeared as in-car technologies that aim to help and complement the human-based carcontrol in order to prevent potential accidents (National Highway Traffic Safety Administration, 2005). This procedure may generate a faster and controlled braking response than the one made by the driver alone, possibly preventing a potential accident
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