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Smart automotive lighting for vehicle safety

Yu, Shun-Hsiang, Shih, Oliver, Tsai, Hsin-Mu, Wisitpongphan, Nawaporn, Roberts, Richard D.·2013·IEEE Communications Magazine

Abstract

It is believed that vehicle-to-vehicle (V2V) communications and accurate positioning with sub-meter error could bring vehicle safety to a different level. However, to this date it is still unclear whether the envisioned V2V standard, dedicated short-range communications, can become available in commercially available vehicle products, while widely available consumergrade GPS receivers do not provide the required accuracy for many safety applications. In this article, combining visible light communications and visible light positioning, we propose the use of smart automotive lighting in vehicle safety systems. These lights would be able to provide the functions of illumination and signaling, reliable communications, and accurate positioning in a single solution. The proposed solution has low complexity, and is shown to be scalable in high vehicle density and fast topology changing scenarios. In this article, we also present several design guidelines for such a system, based on the results of our analytic and empirical studies. Finally, evaluation of our prototype provides evidence that the system can indeed detect potential risks in advance and provide early warnings to the driver in real-world scenarios, lowering the probability of traffic accidents.

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Metadata

Authors
Yu, S.-H., Shih, O., Tsai, H.-M., Wisitpongphan, N., & Roberts, R. D.
Venue
IEEE Communications Magazine
Published
2013-12
Volume
51
Issue
12
Pages
50-59
ISSN
  • 0163-6804 (ISSN-L)
  • 1558-1896
OpenAlex
W2020859417
Cited by
180
References
0
FWCI
12.38
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Smart automotive lighting for vehicle safety. https://app.cochat.ai/papers/10.1109/mcom.2013.6685757
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Yu, S.-H., Shih, O., Tsai, H.-M., Wisitpongphan, N., & Roberts, R. D. (2013). Smart automotive lighting for vehicle safety. IEEE Communications Magazine. https://doi.org/10.1109/mcom.2013.6685757

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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.