An In-Vehicle Warning Information Provision Strategy for V2V-Based Proactive Traffic Safety Management
Abstract
The availability of vehicle interaction data, which is obtained by an in-vehicle forward collision warning system, including spacing between the leading and the following vehicle and time-to-collision, provides a valuable opportunity to predict crash risks in real time. When this opportunity is combined with connected vehicle technologies including vehicle-to-vehicle wireless communications, it is expected that more effective crash prevention would be achievable by providing predictive warning information as a part of proactive traffic safety management (PTSM). The purpose of this study is to develop a more reliable in-vehicle warning information provision strategy based on the prediction of crash risks using vehicle interaction data. A crash risk prediction model based on a long short-term memory was able to predict the crash risk after 3 seconds with a mean absolute percentage error of 8% using the data for the past 5 seconds. The predicted crash risk data were applied to derive the optimal threshold for triggering in-vehicle warning information, which is the essence of the proposed warning provision strategy. This study defined three indicators to evaluate the reliability of warning information: correct detection rate (CDR), detection failure rate (DFR), and information provision rate (IPR). An exemplar analysis result showed that the optimal threshold to minimize IPR in a situation where CDR and DFR are 100% and 0%, respectively, was identified as 0.69. The proposed methodology that predicts crash risks in real time and provides V2V-based warning information in a more proactive manner is expected to mitigate the crash risk significantly.
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Jo et al. developed a strategy that anticipates crash risks based on ICV communication data.
“Following this, Jo et al. [97] developed a strategy that anticipates crash risks based on ICV communication data.”
From Vehicle-to-Everything Communication in Intelligent Connected Vehicles: A Survey and Taxonomy (2025) · cites this paper as [97]
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- Authors
- Jo, Y., Jang, J., Ko, J., & Oh, C.
- Affiliations
- Hanyang University (KR)
- Korea Institute of Civil Engineering and Building Technology (KR)
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2022-10
- Volume
- 23
- Issue
- 10
- Pages
- 19387-19398
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W4221070001
- Cited by
- 30
- References
- 51
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 9 · 2023: 6 · 2022: 2
- FWCI
- 4.11
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Intelligent Transportation Systems (publishedVersion)
- Funders
- Korea Agency for Infrastructure Technology Advancement (KAIA) Grant by the Ministry of Land, Infrastructure and Transport (21AMDP-C160881-01)
- Topics
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Traffic and Road Safety
- Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Vehicular Ad Hoc Networks (VANETs)
- Keywords
- vehicle-to-vehicle communication, crash risk prediction, long short-term memory, forward collision warning, time-to-collision, inter-vehicle distance, connected vehicles, crash prevention
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