Identification of Driver Braking Intention Based on Long Short-Term Memory (LSTM) Network

Abstract

Driving intention identification is a key technology which can improve the adaptability of the intelligent driver assistance systems and the energy efficiency of electric vehicles. This article proposes a novel method for identifying the driver braking intention. In order to improve the identification accuracy of driving intention, a braking intention identification model based on Long Short-Term Memory (LSTM) Network is constructed. The data of slight braking, normal braking and hard braking that can use for offline training are obtained through tests on real vehicle at Chang'an University vehicle performance testing ground. Support vector machine - recursive feature elimination (SVM-RFE) algorithm is used to select the characteristic parameter of braking intention identification model. The random search is subsequently used to optimize the hyper-parameters of LSTM. LSTM-based and Gaussian Hidden Markov Model (GHMM)-based model under different time window are used to identify braking intention of slight braking, normal braking and hard braking respectively. The results show that the Precision, Recall, F-measure, Accuracy of the braking intention identification model which propose in this paper based on LSTM are better than that of the braking intention identification model based on GHMM. Moreover, the Recall and Accuracy of the LSTM-based braking intention identification models are above 0.95, indicating the good ability of intention identification.

Keywords

• Braking intention recognition
• driving safety
• driver assistance system
• LSTM network
• accuracy and real-time