Misunderstanding of driver correction behaviors (DCB) is the primary reason for false warnings of lane-departureprediction systems. We propose a learning-based approach to predicting unintended lane-departure behaviors (LDB) and the chance for drivers to bring the vehicle back to the lane. First, in this approach, a personalized driver model for lanedeparture and lane-keeping behavior is establ...

Hidden Markov Model (HMM) is often regarded as the dynamical model of choice in many fields and applications. It is also at the heart of most state-of-theart speech recognition systems since the 70’s. However, from Gaussian mixture models HMMs (GMMHMM) to deep neural network HMMs (DNN-HMM), the underlying Markovian chain of state-of-the-art models did not changed much. The “left-to-right” topol...

In this paper a signal modeling technique based upon finite mixture autoregressive probabilistic functions of Markov chains is developed and applied to the problem of speech recognition, particularly speaker-independent recognition of isolated digits. Two types of mixture probability densities are investigated: finite mixtures of Gaussian autoregressive densities (GAM) and nearest-neighbor part...

An investigation into the use of Bayesian learning of the parameters of a multivariate Gaussian mixture density has been carried out. In a framework of continuous density hidden Markov model (CDHMM), Bayesian learning serves as a uni ed approach for parameter smoothing, speaker adaptation, speaker clustering and corrective training. The goal is to enhance model robustness in a CDHMM-based speec...

Wi-Fi fingerprinting is one of the methods that are widely used to provide Location Based Services (LBS). Gaussian, or a mixture Gaussians, preferred model by for LBS. Nevertheless, Received Signal Strength Intensity (RSSI) histograms skewed, and Gaussian not well suited modeling data when their histogram skewed. In addition, another important characteristic present in RSSI temporal series auto...

in this paper, an interactive model for individual normal behaviour of drivers is presented in which the mutual effect of vehicles has been incorporated. temporal features obtained from vehicles tracking and their motion history is utilized for generating a model of normal behaviour. because of non-stationarity of behaviour, hidden markov model has been used for interactive model. this model ha...

Language models assign probabilities to strings of symbols. Their interpretation is reviewed and applied to text classification. A language recogniser is constructed from Bayes’ theorem and a simple bigram model. This provides near perfect results on sentences of text and motivates a mixture language model. Hidden Markov models (HMM) are reviewed as a method of capturing order over different le...