Alarm effectiveness in driver-centred collision-warning systems
Abstract
The potential use of systems that seek to communicate a warning of impending collision directly to the driver is examined. Technological advances in collision-warning systems include reliable, low-cost radars, sensors with low noise levels, and the development of accurate detection algorithms for particular crash types, e.g. rear-end collisions. However, fundamental practical constraints make perfect sensor detection difficult to achieve. Imperfect detection conflates the false alarm rate and experience with other technologies confirms driver aversion to false warnings. Although sensitive alarm systems with high detection rates and low false alarm rates have been developed, the posterior probability of a collision given an alarm can be quite low because of the low base rate of collision events. As a result, only a small proportion of alarms will represent true collision scenarios. These and other factors can conspire to reduce alarm effectiveness in collision-warning systems. The problem is illustrated analytically and potential solutions are advanced.
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To ensure alert reliability, the probability that an alarm reflects a true hazardous event must be maximized to the extent possible; this can be examined by combining signal detection theory and Bayesian statistics.
“To ensure alert reliability, the probability that an alarm reflects a true hazardous event must also be maximized to the extent possible: this can be examined by combining signal detection theory and Bayesian statistics [70].”
From A model for types and levels of human interaction with automation (2000) · cites this paper as [70]
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- Authors
- PARASURAMAN, R., HANCOCK, P. A., & OLOFINBOBA, O.
- Affiliations
- Catholic University of America (US)
- Venue
- Ergonomics
- Publisher
- Informa UK Limited
- Published
- 1997-03
- Volume
- 40
- Issue
- 3
- Pages
- 390-399
- ISSN
- 0014-0139 (ISSN-L)
- 1366-5847
- OpenAlex
- W2119374926
- Cited by
- 205
- References
- 28
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 4 · 2023: 3 · 2022: 3 · 2021: 9 · 2020: 8 · 2019: 8 · 2018: 7 · 2017: 5 · 2016: 11 · 2015: 13 · 2014: 13 · 2013: 7 · 2012: 7
- FWCI
- 9.25
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Ergonomics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Traffic and Road Safety
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- collision warning systems, false alarm rate, driver distraction
- MeSH terms
- Accidents, Traffic, Algorithms, Decision Support Techniques, Equipment Design, Humans
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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