Driver Distraction Assessment Using Driver Modeling

Hermannstadter, Peter; Yang, Bin

doi:10.1109/smc.2013.629

Cited by 15 publications

(3 citation statements)

References 18 publications

(40 reference statements)

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“…The last one showed better accuracy. In [29], the control theoretic driver model based on the literary physiological aspect was induced by the driver's behavior predictive model design. This model was compared with a real driver performance.…”

Section: Related Work and Problem Statementmentioning

confidence: 99%

Detection and Evaluation of Driver Distraction Using Machine Learning and Fuzzy Logic

Aksjonov

Nedoma

Vodovozov

et al. 2019

IEEE Trans. Intell. Transport. Syst.

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In addition to vehicle control, drivers often perform secondary tasks that impede driving. Reduction of driver distraction is an important challenge for the safety of intelligent transportation systems. In this paper, a methodology for detection and evaluation of driver distraction while performing secondary tasks is described and an appropriate hardware and software environment is offered and studied. The system includes a model of normal driving, a subsystem for measuring the errors from the secondary tasks, and a module for total distraction evaluation. A new machine learning algorithm defines driver performance in lane keeping and speed maintenance on a specific road segment. To recognize the errors, a method is proposed, which compares normal driving parameters with ones obtained while conducting a secondary task. To evaluate distraction, an effective fuzzy logic algorithm is used. To verify the proposed approach, a case study with driver-in-the-loop experiments was carried out, in which participants performed the secondary task, namely chatting on a cell phone. The results presented in this research confirm its capability to detect and to precisely measure a level of abnormal driver performance.

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Section: Related Work and Problem Statementmentioning

confidence: 99%

Detection and Evaluation of Driver Distraction Using Machine Learning and Fuzzy Logic

Aksjonov

Nedoma

Vodovozov

et al. 2019

IEEE Trans. Intell. Transport. Syst.

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“…Classically, Direct Policy Estimation (DPE) by means of regression is employed for this purpose e.g. as in [2]. Difficulties in the application of DPE arise for system states that are not contained in the collected data.…”

Section: A Motivationmentioning

confidence: 99%

Exact Maximum Entropy Inverse Optimal Control for modeling human attention switching and control

Schmitt

Bieg

Manstetten

et al. 2016

2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Maximum Causal Entropy (MCE) Inverse Optimal Control (IOC) has become an effective tool for modeling human behavior in many control tasks. Its advantage over classic techniques for estimating human policies is the transferability of the inferred objectives: Behavior can be predicted in variations of the control task by policy computation using a relaxed optimality criterion. However, exact policy inference is often computationally intractable in control problems with imperfect state observation. In this work, we present a model class that allows modeling human control of two tasks of which only one be perfectly observed at a time requiring attention switching. We show how efficient and exact objective and policy inference via MCE can be conducted for these control problems. Both MCE-IOC and Maximum Causal Likelihood (MCL)-IOC, a variant of the original MCE approach, as well as Direct Policy Estimation (DPE) are evaluated using simulated and real behavioral data. Prediction error and generalization over changes in the control process are both considered in the evaluation. The results show a clear advantage of both IOC methods over DPE, especially in the transfer over variation of the control process. MCE and MCL performed similar when training on a large set of simulated data, but differed significantly on small sets and real data.

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“…The model has been successfully used in the synthesizing a haptic steering system [11] and in discriminating various types of distraction through analyzing parametric variations [10], [12], [13]. However, the model has been designed and identified in situations where the human drives alone.…”

Section: Introductionmentioning

confidence: 99%

Towards a Driver Model to Clarify Cooperation Between Drivers and Haptic Guidance Systems

Zhao

Chevrel

Claveau

et al. 2020

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Driver Distraction Assessment Using Driver Modeling

Cited by 15 publications

References 18 publications

Detection and Evaluation of Driver Distraction Using Machine Learning and Fuzzy Logic

Detection and Evaluation of Driver Distraction Using Machine Learning and Fuzzy Logic

Exact Maximum Entropy Inverse Optimal Control for modeling human attention switching and control

Towards a Driver Model to Clarify Cooperation Between Drivers and Haptic Guidance Systems

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