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Driver Behaviour RecognitionUsing Hidden Markov Models

Driver Behaviour RecognitionUsing Hidden Markov Models
Haghighi Osgouei, Reza
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In this work we addressed the problem of modelling human driving behavior using hidden Markov models (HMMs). It is part of a bigger objective towards capturing and transferring driving skills from an expert driver to a novice trainee. We believe driving behaviors are in result of driver's decision making rules. So we drew our attention to identify and recognize driver's decisions or in another sense driving rules using driving time-series signals. For this end, first a driving simulator based on a commercial racing wheel is developed to simulate a desired driving task. The required driving signals including acceleration pedal position, steering wheel angle, velocity and heading of the vehicle are collected using the driving simulator. Then inspired by the fact that the only variables a driver has control on them are velocity and heading, their first-order derivative are extracted as the two most important features of driving patterns. Following the same inspiration, we developed an automatic segmentation method to detect the local extrema of controlling variables and divide data samples into a number of segments. We suggest during each segment, the driver keeps the pedal and wheel operations unchanged. Not all data segments come from different sources
there might be some criteria to group similar segments. In this line we proposed three partitioning methods, threshold-based, GMM-based, and hierarchical, all originated from dividing the two dimensional feature space into a number of classes. In our belief, data classes are the time-domain realization of driving behaviors. According to each class, the parameters of one HMM are optimized to be used later for recognizing driving behaviors. The achieved high average correct classification rate, between 85% to 95% depend on the partitioning criteria, reveals the efficacy of proposed approach in classifying and recognizing driving behaviors. Finally we made use of behavior recognizers to compare an expert and a novice driver's performances in order to provide some feedback. Two methods one road-dependent and another road-independent are proposed for this end. The evaluation results proved the applicability of proposed methods.
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