Literature DB >> 33770718

Drivers' visual-distracted take-over performance model and its application on adaptive adjustment of time budget.

Qingkun Li1, Lian Hou2, Zhenyuan Wang3, Wenjun Wang4, Chao Zeng5, Quan Yuan6, Bo Cheng7.   

Abstract

There are certain situations that automated driving (AD) systems are still unable to handle, preventing the implementation of Level 5 AD. Thus, a transition of control, colloquially known as take-over of the vehicle, is required when the system sends a take-over request (TOR) upon exiting the operational design domain (ODD). An adaptive TOR along with good take-over performance requires adjusting the time budget (TB) to drivers' visual distraction state, adhering to a reliable visual-distraction-based take-over performance model. Based on a number of driving simulator experiments, the percentage of face orientation to distraction area (PFODA) and time to boundary at take-over timing (TTBT) were proposed to accurately evaluate the degree of visual distraction based on merely face orientation under naturalistic non-driving related tasks (NDRTs) and to evaluate take-over performance, respectively. In order to elucidate the safety boundary, this study also proposed an algorithm to set a suitable minimum value of the TTBT. Finally, a multiple regression model was built to describe the relationship among PFODA, TB and TTBT along with a corrected coefficient of determination of 0.748. Based on the model, this study proposed an adaptive TB adjustment method for the take-over system.
Copyright © 2021 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Automated driving; Driving performance; Human factors; Take-over; Visual distraction

Mesh:

Year:  2021        PMID: 33770718     DOI: 10.1016/j.aap.2021.106099

Source DB:  PubMed          Journal:  Accid Anal Prev        ISSN: 0001-4575


  2 in total

1.  Driver's Visual Attention Characteristics and Their Emotional Influencing Mechanism under Different Cognitive Tasks.

Authors:  Yaqi Liu; Xiaoyuan Wang; Longfei Chen; Shijie Liu; Junyan Han; Huili Shi; Fusheng Zhong
Journal:  Int J Environ Res Public Health       Date:  2022-04-21       Impact factor: 4.614

2.  Analysis of hazard perception characteristics based on driving behavior considering overt and covert hazard scenarios.

Authors:  Tianzheng Wei; Tong Zhu; Chenxin Li; Haoxue Liu
Journal:  PLoS One       Date:  2022-04-01       Impact factor: 3.240

  2 in total

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