Ego-Vehicle Speed Prediction Using a Long Short-Term Memory Based Recurrent Neural Network

Abstract

for predictive powertrain control, accurate prediction of vehicle speed is required. As vehicle speed prediction is affected by the driver's response to numerous driving conditions under uncertainty, the development of an accurate model is quite challenging. This paper proposes an ego-vehicle speed prediction model using a long short-term memory (LSTM) based recurrent neural network (RNN). The proposed model uses various inputs to increase the prediction accuracy: internal vehicle information, relative speed and distance to the vehicle ahead measured by a radar sensor, and the ego-vehicle location estimated by the GPS signal and B-spline roadway model. The LSTM based RNN model predicts the ego-vehicle speed for 15 seconds by using inputs from the past 30 seconds. The model was evaluated by real driving data for three scenarios: car-following, sharp curve road, and full path. In all scenarios, the radar sensor and the information of the location of the ego-vehicle contribute to improvement of the speed prediction accuracy. Thus, we conclude that for application of the predictive powertrain control, besides the internal vehicle information, the radar sensor, and the location of the ego-vehicle information are critical inputs to the speed prediction model.