Download PDFOpen PDF in browserA modified Hidden Markov Model (HMM)-based state machine model for driving behavior recognition: Effectiveness of features using different sub-HMMs13 pages•Published: July 12, 2024AbstractDriving 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).Keyphrases: Advanced Driving Assistance Systems, Driving behavior recognition, feature selection, Hidden Markov Model, state machines, Sub-Hidden Markov Model In: Kenneth Baclawski, Michael Kozak, Kirstie Bellman, Giuseppe D'Aniello, Alicia Ruvinsky and Candida Da Silva Ferreira Barreto (editors). Proceedings of Conference on Cognitive and Computational Aspects of Situation Management 2023, vol 102, pages 20--32
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