| EGO-VEHICLE SPEED PREDICTION USING A LONG SHORT-TERM MEMORY BASED RECURRENT NEURAL NETWORK |
Kyuhwan Yeon1
, Kyunghan Min1, Jaewook Shin1
, Myoungho Sunwoo1, Manbae Han2
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1Hanyang University
2Keimyung University |
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| ABSTRACT |
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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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Corresponding Author.
Manbae Han , Email. 
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