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Driving behavior estimations play a significant role in the development of Advanced Driving Assistance Systems (ADASs). The estimations are often developed using ma- chine learning-based approaches, which are influenced by different factors, such as input variables and design of methods. However, developing a suitable configuration can be complicated. In this contribution, an improved Hidden Markov Model (HMM)-based state machine model is introduced for the recognition of lane changing behaviors. Adapting a previously developed HMM model, the model consists of different sub-HMMs which are fused to develop the HMM estimations. A prefilter is introduced in the HMM to quantize the input variables into segments of observed sequences that distinguish different driving situations. Hence, optimization of the prefilter is performed. Different from the previous work, a state machine model is incorporated to develop the final behavior estimation using the estimations of the HMM model. To evaluate the estimation effectiveness, different driving features (inputs) are evaluated by using different combinations of sub-HMMs. Ex- perimental driving data based on six drivers used for the application of the method show that the approach generates adequate accuracy (ACC), detection rates (DR), and false alarm rates (FAR).
David et al. (Fri,) studied this question.