Driver state monitoring employing the Human Performance Envelope model

The field of driving safety is significantly influenced by human factors, particularly within the automated driving context. This study explores the application of the Human Performance Envelope (HPE) model to Driver Monitoring Systems (DMS), with the aim of mapping and interpreting driver parameters. The study is groundedin the utilisation of an instrumented steering wheel, capable of detecting forces, moments and grip applied by the driver during a realistic driving simulation developed at the DriSMi laboratory of the Politecnico di Milano. Seven participants were presented with scenarios encompassing the transition from automated to manual driving, experiencing critical situations such as a roadwork site and emergency braking. Preliminary results on instrumented steering wheel analysis demonstrate the potential of the measured parameters to construct HPE maps, which may contribute to the identification of performance decrements and the enhancement of safety in self-driving vehicles. This approach offers novel perspectives to integrate human factors into DMS.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-11-21
DOI
https://doi.org/10.5281/zenodo.17670615
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
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article

Driver state monitoring employing the Human Performance Envelope model

Zenodo (CERN European Organization for Nuclear Research)
Human-Automation Interaction and Safety
article

Driver state monitoring employing the Human Performance Envelope model

article en

Abstract

The field of driving safety is significantly influenced by human factors, particularly within the automated driving context. This study explores the application of the Human Performance Envelope (HPE) model to Driver Monitoring Systems (DMS), with the aim of mapping and interpreting driver parameters. The study is groundedin the utilisation of an instrumented steering wheel, capable of detecting forces, moments and grip applied by the driver during a realistic driving simulation developed at the DriSMi laboratory of the Politecnico di Milano. Seven participants were presented with scenarios encompassing the transition from automated to manual driving, experiencing critical situations such as a roadwork site and emergency braking. Preliminary results on instrumented steering wheel analysis demonstrate the potential of the measured parameters to construct HPE maps, which may contribute to the identification of performance decrements and the enhancement of safety in self-driving vehicles. This approach offers novel perspectives to integrate human factors into DMS.

Zenodo (CERN European Organization for Nuclear Research)
University of Modena and Reggio Emilia (IT), University of Bologna (IT), Politecnico di Milano (IT)
Openalex Percentile: Top 37%
Human-Automation Interaction and Safety
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