Construction of personalized driver model for car-following behavior on highways using LSTM

Abstract

In recent years, automatic driving and advanced driving support systems have become more widespread; however, control systems do not consider the driving characteristics of each driver. This study constructed a personalized driver model for following forward vehicles on highways using LSTM (Long Short-Term Memory). In particular, the setting parameters for training with LSTM was focused upon and consequently, the relationship between the setting parameters and model accuracy was evaluated. The LSTM parameters considered were the number of hidden units, learning rate, number of epochs, and number of LSTM layers. Further, the driving data were measured using a driving simulator. Moreover, the model was evaluated in terms of accuracy using the coefficient of determination, and 125 combinations of parameters were compared using the mean value of the coefficient of determination and its significant difference. The order of parameter comparisons was performed considering the accuracy and learning time of the model. Consequently, the parameter set that is likely to allow efficient building of a highly accurate driver model was determined. Applying the parameter evaluation in this study contributes to the development of personalized driver models with high accuracy.

Keywords

• driver model
• neural network
• long short-term memory (lstm)
• driving simulator
• adaptive cruise control (acc)
• automated driving
• advanced driver assistance system (adas)