1P1-B14 ノンパラメトリックベイズに基づく協調学習型運転行動モデリング
詳細
In this paper, we propose a novel framework for personalized driver behavior modeling with Bayesian learning methods. Specifically, the proposed framework, formulated as Dirichlet process mixtures, achieves simultaneous categorization, identification and adaptation of driver models from driving records of multiple drivers. We also provide a histogram quantization technique to accelerate the speed of inference process. The experimental results using driving records of multiple drivers under car following situation show that our method is superior over the other conventional methods in view of accuracy and inference efficiency.
