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International Journal of Automotive Technology > Volume 20(4); 2019 > Article
International Journal of Automotive Technology 2019;20(4): 713-722.
Kyuhwan Yeon1, Kyunghan Min1, Jaewook Shin1, Myoungho Sunwoo1, Manbae Han2
1Hanyang University
2Keimyung University
PDF Links Corresponding Author.  Manbae Han , Email. mbhan2002@kmu.ac.kr
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: carfollowing, sharp curve road, and full path. In all scenarios, the radar sensor and the information of the location of the egovehicle 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.
Key Words: Vehicle speed prediction, Recurrent neural network, Long short-term memory, Predictive powertrain control
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