Driving behavior classification using long short term memory networks

Official URL: https://dx.doi.org/10.23919/EETA.2019.8804534

Researchers in the automotive industry aim to enhance the performance, safety and energy management of intelligent vehicles with driver assistance systems. The performance of such systems can be improved with a better understanding of driving behaviors. In this paper, a driving behavior recognition algorithm is developed with a Long Short Term Memory (LSTM) Network using driver models of IPG's TruckMaker. Six driver models are designed based on longitudinal and lateral acceleration limits. The proposed algorithm is trained with driving signals of these drivers controlling a realistic truck model with five different trailer loads on an artificial training road. This training road is designed to cover possible road curves that can be seen in freeways and rural highways. Finally, the algorithm is tested with driving signals that are collected with the same method on a realistic road. Results show that the LSTM structure has a substantial capability to recognize dynamic relations between driving signals even in small time periods.