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Estimation de la distraction fondée sur un modèle dynamique de conducteur : principes et algorithmes

Ablamvi Ameyoe 1, 2 
2 Commande
IRCCyN - Institut de Recherche en Communications et en Cybernétique de Nantes
Abstract : Distracted driving is one of the important factors that cause road accidents. The detection of the driver’s state of distraction in the industrial context and at low-cost leads to privilege the indicators based on sensors that are already available on the vehicle. However,current systems are generally not reliable enough, especially because the observed magnitudes to achieve detection are quite far from a purely physiological phenomenon distraction. This led us to propose solutions based on a cybernetic driver model that represent the visual and motor process involved in the lateral control of the vehicle. The parameters of this model have been estimated by conducting successively identification exploiting data packets and recursive identification, the latter allowing to track continuously the parametric evolution over time. Then, three approaches were considered to model or estimate the state of distraction, by modeling alternately thedistraction as a disturbance affecting parameters, the output or the input of the cybernetic model of the driver:Approach 1 - The distraction is modeled as an additive disturbance on the model output. The experimental output, the driver steering wheel torque, is then compared with the predicted steering wheel torque to generate the torque prediction error that is sensitive to the state of distraction.Approach 2 - The distraction is modeled as disturbances that affect the model parameters. The analysis of these parameters identified during normal and distracted driving periods showed that the parameters’ variation depends effectively on the driver’s state of distraction.Approach 3 - Distraction is modeled as an additive disturbance on the input of the model. The estimate of this disturbance is also a significant residue, sensitive to the state of distraction. The principles and algorithms proposed for estimating the state of distraction were validated using experimental data collected during a test campaign conducted on a fixed-base driving simulator, involving 35 drivers. The test conditions alternated normal driving phases and prone to distractions of various kinds: cognitive distractions, visual, visual-motor and motor. The three proposed approaches give similar and consistent results between them.
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Submitted on : Thursday, November 10, 2016 - 3:50:07 PM
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  • HAL Id : tel-01395282, version 1

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Ablamvi Ameyoe. Estimation de la distraction fondée sur un modèle dynamique de conducteur : principes et algorithmes. Automatique. Ecole des Mines de Nantes, 2016. Français. ⟨NNT : 2016EMNA0271⟩. ⟨tel-01395282⟩

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