Analysis of Driving Patterns and On-Board Feedback-Based Training for Proactive Road Safety Monitoring

Pozueco, Laura; Gupta, Nishu; Pañeda, Xabiel G.; Garcı́a, Roberto; Tuero, Alejandro G.; Melendi, David; Rionda, Abel; Magaña, Víctor Corcoba

doi:10.1109/thms.2020.3027525

Cited by 12 publications

(1 citation statement)

References 26 publications

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“…However, humans are diverse, and it is impractical to utilize the same criteria to evaluate the state of different people. This is demonstrated in [235], which established a framework for analyzing the driving behavior of professional drivers and evaluating the impact of novel safe driving concepts on different driver profiles. The results revealed that drivers with varying levels of experience exhibited varying levels of performance when applying safe and anticipative driving behaviors.…”

Section: G Personalized Driver Behaviour Recognitionmentioning

confidence: 99%

Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles

Lou

Xing

et al. 2022

IEEE Trans. Intell. Veh.

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Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the design of the automated vehicle, whereas the digitization of the human driver, who plays an important role in driving, is largely ignored. Furthermore, previous driver-related tasks are limited to specific scenarios and have limited applicability. Thus, a novel concept of a driver digital twin (DDT) is proposed in this study to bridge the gap between existing automated driving systems and fully digitized ones and aid in the development of a complete driving human cyber-physical system (H-CPS). This concept is essential for constructing a harmonious human-centric intelligent driving system that considers the proactivity and sensitivity of the human driver. The primary characteristics of the DDT include multimodal state fusion, personalized modeling, and time variance. Compared with the original DT, the proposed DDT emphasizes on internal personality and capability with respect to the external physiological-level state. This study systematically illustrates the DDT and outlines its key enabling aspects. The related technologies are comprehensively reviewed and discussed with a view to improving them by leveraging the DDT. In addition, the potential applications and unsettled challenges are considered. This study aims to provide fundamental theoretical support to researchers in determining the future scope of the DDT system.

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Section: G Personalized Driver Behaviour Recognitionmentioning

confidence: 99%