Predicting Driver Lane Change Maneuvers Using Driver's Face

In this paper, we will present our project concerning realizing a system of predicting maneuvers before the vehicle turns through using a dataset containing videos of drivers in different situations and different maneuvers, as well as information on the environment, such as speed, empty lines and the existence of an artifact. After preparing the dataset, we built our CNN-LSTM model based on convolutional and recurrent layers. Our CNN-LSTM model allowed us to predict the maneuver with an accuracy of 94.1% and 3.75 seconds before the turn. Finally, For our model to be robust, we tried to detect anomalies and replace them with more meaningful values. We also tested our model by adding noise to the images.