リカレントニューラルネットワークによる運転時の人間行動モデルの性能評価

―モデル予測制御を用いた場合との比較検討―

抄録

This article discusses the results of evaluating the performance of a recurrent neural network (RNN)-based human behavior model proposed for estimating human behavior that can be used for driver assistance. Parameters of the RNN-based model were identified so that the response of the closed-loop system can reproduce the actual output acquired through the positioning experiment with human steering wheel operation. The performance is evaluated in both the time and frequency domains, compared to the model based on model predictive control (MPC). As a result, it was confirmed that the RNN-based model reproduced human behavior with almost all the same accuracy as the MPC-based model.