Cited by 30Traffic and Road SafetyTraffic control and managementVehicular Ad Hoc Networks (VANETs)

An In-Vehicle Warning Information Provision Strategy for V2V-Based Proactive Traffic Safety Management

Jo, Young, Jang, Jiyong, Ko, Jieun, Oh, Cheol·2022·IEEE Transactions on Intelligent Transportation Systems

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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Crossref · 10.1109/tits.2022.3156923

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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
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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