Articles | Volume 15, issue 1
https://doi.org/10.5194/jsss-15-27-2026
https://doi.org/10.5194/jsss-15-27-2026
Regular research article
 | 
19 Feb 2026
Regular research article |  | 19 Feb 2026

Recognising wild animals on roads: multisensor systems for accident avoidance

Michael Schneider, Hubert Mantz, Thomas Walter, Mike Montoya-Capote, Jonas Berger, Andreas Reichel, and Nils Hollmach

Cited articles

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Flir: Boson640, https://www.flir.de/products/boson/ (last access: 18 October 2024), 2024. a
FVA: Pilotprojekt Elektronische WildwarnanlageB292 bei Aglasterhausen, https://www.fva-bw.de/fileadmin/scripts/forschung/wg/081014wildwarn_ber.pdf (last access: 17 October 2024), 2008. a, b
Gil, G.-T., Lee, J. Y., and Cho, D.-H.: Estimation of Path Loss Parameters of a Sub-Terahertz Wireless Channel Using Monostatic Radar, IEEE Access, 9, 52654–52663, https://doi.org/10.1109/ACCESS.2021.3070378, 2021. a
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Short summary
More traffic leads to more accidents involving wildlife, especially on rural roadcuts through habitats. Solutions like wildlife bridges and fences are needed, but there is no comprehensive solution yet. We have developed a system to detect and assess wildlife, including deer. This technology can work at night and in fog as animals cross roads in poor visibility. Radar sensors and infrared cameras are our solution.
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