The DOI matches its Crossref record.
Back to the future: Brake reaction times for manual and automated vehicles
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.
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Citations
Cited by
Milakis, D., van Arem, B., & van Wee, B. (2017) · Journal of Intelligent Transportation Systems · doi:10.1080/15472450.2017.1291351 · Cited by 963
Multisensory In-Car Warning Signals for Collision Avoidance
Ho, C., Reed, N., & Spence, C. (2007) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872007x249965 · Cited by 235
Metadata
- Authors
- Young, M. S., & Stanton, N. A.
- Venue
- Ergonomics
- Publisher
- Informa UK Limited
- Published
- 2007-01-15
- Volume
- 50
- Issue
- 1
- Pages
- 46-58
- ISSN
- 0014-0139 (ISSN-L)
- 1366-5847
- OpenAlex
- W2011337802
- Cited by
- 180
- References
- 0
- FWCI
- 4.21
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
