Literature DB >> 17178651

Back to the future: brake reaction times for manual and automated vehicles.

Mark S Young1, Neville A Stanton.   

Abstract

Rear-end collisions are often quoted as being a major cause of road traffic accidents. In response to this, a great deal of ergonomics research effort has been directed towards the analysis of brake reaction times. However, the engineering solution has been to develop advanced systems for longitudinal control, which, it is argued, will mitigate the problem of rear-end collisions. So far, though, there have been few empirical studies to determine how brake reaction times will be affected by such vehicle automation. This paper presents a literature review summarizing the current state of knowledge about driver responses in non-automated vehicles. The review covers driver factors, vehicle factors and situational factors. Following the review, some empirical data are presented from a driving simulator experiment assessing brake reaction times of skilled and unskilled drivers under two different levels of automation. When compared to previous data gathered during manual driving, there seems to be a striking increase in reaction times for these automated conditions. Implications for the design and safety of automated vehicle systems are discussed.

Mesh:

Year:  2007        PMID: 17178651     DOI: 10.1080/00140130600980789

Source DB:  PubMed          Journal:  Ergonomics        ISSN: 0014-0139            Impact factor:   2.778


  7 in total

1.  Active and passive fatigue in simulated driving: discriminating styles of workload regulation and their safety impacts.

Authors:  Dyani J Saxby; Gerald Matthews; Joel S Warm; Edward M Hitchcock; Catherine Neubauer
Journal:  J Exp Psychol Appl       Date:  2013-09-16

2.  The Challenges of Partially Automated Driving.

Authors:  Stephen M Casner; Edwin L Hutchins; Don Norman
Journal:  Commun ACM       Date:  2016-04-26       Impact factor: 4.654

3.  Human-Like Lane Change Decision Model for Autonomous Vehicles that Considers the Risk Perception of Drivers in Mixed Traffic.

Authors:  Chang Wang; Qinyu Sun; Zhen Li; Hongjia Zhang
Journal:  Sensors (Basel)       Date:  2020-04-16       Impact factor: 3.576

4.  Electrophysiological frequency domain analysis of driver passive fatigue under automated driving conditions.

Authors:  Yijing Zhang; Jinfei Ma; Chi Zhang; Ruosong Chang
Journal:  Sci Rep       Date:  2021-10-13       Impact factor: 4.379

5.  Effects of a Motion Seat System on Driver's Passive Task-Related Fatigue: An On-Road Driving Study.

Authors:  Seunghoon Lee; Minjae Kim; Hayoung Jung; Dohoon Kwon; Sunwoo Choi; Heecheon You
Journal:  Sensors (Basel)       Date:  2020-05-08       Impact factor: 3.576

6.  Research on the Influence of Vehicle Speed on Safety Warning Algorithm: A Lane Change Warning System Case Study.

Authors:  Rui Fu; Yali Zhang; Chang Wang; Wei Yuan; Yingshi Guo; Yong Ma
Journal:  Sensors (Basel)       Date:  2020-05-08       Impact factor: 3.576

7.  A toolbox for automated driving on the STISIM driving simulator.

Authors:  Alexander Eriksson; Joost de Winter; Neville A Stanton
Journal:  MethodsX       Date:  2018-08-15
  7 in total

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