Prediction of Next Contextual Changing Point of Driving Behavior Using Unsupervised Bayesian Double Articulation Analyzer

• Published in: 2014 IEEE Intelligent Vehicles Symposium Proceedings pp. 924 - 931
• Main Authors: Nagasaka, Shogo, Taniguchi, Tadahiro, Hitomi, Kentarou, Takenaka, Kazuhito, Bando, Takashi
• Format: Conference Proceeding
• Language: English; Japanese
• Published: IEEE 01.06.2014
• Subjects: Computational modeling; Context; Hidden Markov models; Probability; Semiotics; Time series analysis; Vehicles
• ISSN: 1931-0587; 2642-7214
• DOI: 10.1109/IVS.2014.6856468

Future advanced driver assistance systems (ADASs) should observe a driving behavior and detect contextual changing points of driving behaviors. In this paper, we propose a novel method for predicting the next contextual changing point of driving behavior on the basis of a Bayesian double articulation analyzer. To develop the method, we extended a previously proposed semiotic predictor using an unsupervised double articulation analyzer that can extract a two-layered hierarchical structure from driving-behavior data. We employ the hierarchical Dirichlet process hidden semi-Markov model [4] to model duration time of a segment of driving behavior explicitly instead of the sticky hierarchical Dirichlet process hidden Markov model (HDP-HMM) employed in the previous model [13]. Then, to recover the hierarchical structure of contextual driving behavior as a sequence of chunks, we use the Nested Pitman-Yor Language model [6], which can extract latent words from sequences of latent letters. On the basis of the extension, we develop a method for calculating posterior probability distribution of the next contextual changing point by marginalizing potentially possible results of the chunking method and potentially successive words theoretically. To evaluate the proposed method, we applied the method to synthetic data and driving behavior data that was recorded in a real environment. The results showed that the proposed method can predict the next contextual changing point more accurately and in a longer-term manner than the compared methods: linear regression and Recurrent Neural Networks, which were trained through a supervised learning scheme.