Traffic situations leading up to accidents have been shown to be greatly affected by human errors. To reduce
these errors, warning systems such as Driver Alert Control, Collision Warning and Lane Departure Warning
have been introduced. However, there is still room for improvement, both regarding the timing of when a
warning should be given as well as the time needed to detect a hazardous situation in advance. Two factors that
affect when a warning should be given are the environment and the actions of the driver. This study proposes
an artificial neural network-based approach consisting of a convolutional neural network and a recurrent neural
network with long short-term memory to detect and predict different actions of a driver inside a vehicle. The
network achieved an accuracy of 84% while predicting the actions of the driver in the next frame, and an
accuracy of 58% 20 frames ahead with a sampling rate of approximately 30 frames per second.