In this paper, a completely non-intrusive method of monitoring driver drowsiness is described. Because of their abilities to learn behavior and represent very complex relationships, artificial neural networks are the basis of the method presented. Four artificial neural networks are designed based on the hypothesis that the time derivative of force (jerk) exerted by the driver at the steering wheel and accelerator pedal can be used to discern levels of alertness. The artificial neural networks are trained to replicate non-drowsy input, and then tested with unseen data. Data sets that are similar to the training sets will pass through the network with little change, and sets that are different will be changed considerably by the network. Thus, the further the driver's jerk profile deviates from the non-drowsy jerk profile, the greater the error between the input and output of the network will be. The changes in network error with drive time are presented from testing the networks with simulated driving data and the performance of the artificial neural network designs are compared.
Non-Intrusive Driver Drowsiness Monitoring Via Artificial Neural Networks
Sae Technical Papers
SAE World Congress & Exhibition ; 2008
2008-04-14
Aufsatz (Konferenz)
Englisch
Non-intrusive driver drowsiness monitoring via artificial neural networks
Kraftfahrwesen | 2008
|Non-Intrusive Driver Drowsiness Monitoring Via Artificial Neural Networks
British Library Conference Proceedings | 2008
|2008-01-0187 Non-Intrusive Driver Drowsiness Monitoring Via Artificial Neural Networks
British Library Conference Proceedings | 2008
|Driver Drowsiness Detection Using Convolution Neural Networks
Springer Verlag | 2021
|