Recognition of human driving behaviors based on stochastic symbolization of time series signal

This paper describes an imitative learning of driving time series data for intellectual cognition toward future automobiles. The driving pattern primitives consisting of states of the environment, vehicle and driver are symbolized by hidden Markov models (HMMs), which can be used for both recognition and generation of the driving patterns. The relationship among the HMMs can be represented by locating the HMMs in a multidimensional space. The contribution of each variable to the HMM space can be analyzed such that important variables can be selected out of the driving data in order to reduce the size of the HMMs. Moreover, this paper presents a hierarchical model with the HMMs abstracting the primitive driving patterns in the lower layer, and another HMMs abstracting the longterm contextual driving patterns which are representation in the HMM space. Tests with a driving simulator and a actual vehicle demonstrate not only the validity of symbolization of driving pattern primitives, recognition and generation, but also availability of key feature selection. The extended hierarchical model is also proved to have a potential to predict the driving data appropriately.