Hyperparameter optimization of LSTM based Driver’s Aggressive Behavior Prediction Model

Abstract

Traffic safety in the area of Intelligent Transportation System can be improved by predicting driver behavior automatically. Aggressive driving is significant indicator for collision. Several deep learning algorithms are developed to predict the behavior of driver and existing algorithm’s hyper parameters are not optimized to get efficient and accurate solution in predicting driver’s behavior. Hyper parameter optimization of LSTM based model has been proposed to predict the driver behavior accurately by determining optimal hyper parameters which includes window size, learning rate, Number of hidden layers and number of hidden units. Bayesian optimization is built to optimize the hyper parameters. 97.02% of accuracy has been achieved for the proposed model. The result reveals that the proposed model outperforms the existing algorithms and hyper parameters have been optimized. The hyper parameter optimization can improve the accuracy of the LSTM based driver’s aggressive behavior prediction model. The proposed model minimizes False alarm rate and missed alarm rate dramatically compared to traditional algorithms. The proposed model can be implemented in collision avoidance system which helps to present warning to aggressive drivers.