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Cited by 42Video Surveillance and Tracking MethodsAutonomous Vehicle Technology and SafetyAdvanced Neural Network Applications

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A Survey on Multi-Sensor Fusion Perimeter Intrusion Detection in High-Speed Railways

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Shi, Tianyun, Guo, Pengyue, Wang, Rui, Ma, Zhen, Zhang, Wanpeng, Li, Wentao, Fu, Huijin, Hu, Hao·2024·Sensors
DOI 10.3390/s24175463PMC PMC11397917
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Abstract

In recent years, the safety issues of high-speed railways have remained severe. The intrusion of personnel or obstacles into the perimeter has often occurred in the past, causing derailment or parking, especially in the case of bad weather such as fog, haze, rain, etc. According to previous research, it is difficult for a single sensor to meet the application needs of all scenario, all weather, and all time domains. Due to the complementary advantages of multi-sensor data such as images and point clouds, multi-sensor fusion detection technology for high-speed railway perimeter intrusion is becoming a research hotspot. To the best of our knowledge, there has been no review of research on multi-sensor fusion detection technology for high-speed railway perimeter intrusion. To make up for this deficiency and stimulate future research, this article first analyzes the situation of high-speed railway technical defense measures and summarizes the research status of single sensor detection. Secondly, based on the analysis of typical intrusion scenarios in high-speed railways, we introduce the research status of multi-sensor data fusion detection algorithms and data. Then, we discuss risk assessment of railway safety. Finally, the trends and challenges of multi-sensor fusion detection algorithms in the railway field are discussed. This provides effective theoretical support and technical guidance for high-speed rail perimeter intrusion monitoring.

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  • A compromised sensor node, falsified measurement, or disrupted data stream can propagate through operational systems, affecting traffic control, rolling stock safety, and signaling integrity.

    “A compromised sensor node, falsified measurement, or disrupted data stream can propagate through operational systems, affecting traffic control, rolling stock safety, and signaling integrity [1,2,3,4].”

    From Sensor-Based Cyber Risk Management in Railway Infrastructure Under the NIS2 Directive (2025) · cites this paper as [3]

    The Conclusions (section 7; page unavailable) say that unstable, incomplete, or inaccurate monitoring data sharply reduce intrusion-recognition performance and that better monitoring helps protect trains. The paper does not establish that compromised sensor nodes or disrupted data streams propagate through operational systems, or that they affect traffic control or signaling integrity. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

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Metadata

Authors
Shi, T., Guo, P., Wang, R., Ma, Z., Zhang, W., Li, W., Fu, H., & Hu, H.
Affiliations
China Academy of Railway Sciences (CN)
Corresponding author
Guo, Pengyue
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2024-08-23
Volume
24
Issue
17
Pages
5463
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s24175463
PMC
PMC11397917
OpenAlex
W4401809451
Cited by
42
References
2
Citations by year
2026: 23 · 2025: 16 · 2024: 3
FWCI
1.04
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Natural Science Foundation of China (U2268217, DZYF23-40)
  • China Academy of Railway Sciences Project (U2268217, DZYF23-40)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
  • Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords
high-speed rail, perimeter intrusion detection, multi-sensor fusion, point clouds, image-based detection, railway safety, severe weather, risk assessment, intrusion monitoring

About these checks

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.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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