Improving the Safety of Autonomous Road Traffic with the Use of Meteorological Data Collected with UAVs

doi: 10.32560/rk.2021.3.12

Abstract

This study investigates the use of UAVs for the purpose of collecting meteorological data, using this data to generate  improved weather forecast, and utilising the results for  autonomous road traffic. Today, an information gap is  identified in the field of meteorological data from the planetary boundary layer. This data can be of key  importance for improved weather forecasting, as it is the  zone of ground-atmospheric interactions. With today’s  drone technology, acceptable temporal and spatial data  resolution can be achieved, in Hungary 15 data stations can  be used to cover 90% of the country. The market for  weather forecasting services can be estimated at HUF 22 billion economic-societal value, which value can be  unlocked as the market for the proposed system. At the  current level of autonomous technologies, the key question is the identification of road sections, where the autonomous functions can be utilised in a safe manner  under the actual weather conditions. Utilising the proposed system this information can be generated and shared with  the road users. 

Keywords:

UAV meteorology weather forecasting forecast system planetary boundary layer autonomous traffic

How to Cite

[1]
D. Sziroczák, I. Gál, D. Szilágyi, J. Rohács, and D. Rohács, “Improving the Safety of Autonomous Road Traffic with the Use of Meteorological Data Collected with UAVs”, RepTudKoz, vol. 33, no. 3, pp. 155–170, Aug. 2022.

References

1. S. Al Moshin, A. Rahman, N. Islam, Weather Monitoring IoT Drone. Dhaka, Bangladesh, Daffodil International University, 2020.

2. Bartholy J. et al., Meteorológiai alapismeretek. Budapest, ELTE, 2013.

3. Black Swift Technologies, S2 UAV Datasheet. Online: https://bst.aero/wp-content/uploads/2020/10/S2-Data-Sheet-Oct2020-web.pdf

4. Bottyán Zs., A közfeladatokat ellátó repülések meteorológiai biztosításának kérdései. Budapest, NKE, 2017.

5. Bottyán Zs. et al., „Measuring and Modeling of Hazardous Weather Phenomena to Aviation Using the Hungarian Unmanned Meteorological Aircraft System (HUMAS),” Időjárás, 119. évf. 3. sz. pp. 307–335. 2015.

6. D. Leuenberger et al., „Improving High-Impact Numerical Weather Prediction with Lidar and Drone Observations,” Bulletin of the American Meteorological Society, Vol. 101, No. 7, pp. E1036–E1051. 2020. Online: https://doi.org/10.1175/BAMS-D-19-0119.1 Online: https://doi.org/10.1175/BAMS-D-19-0119.1

7. European Commission, EU Transport in Figures. Luxembourg, Publications Office of the EU, 2018.

8. EuroStat, Passenger Mobility Statistics. 2021. Online: https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Passenger_mobility_statistics

9. International Transport Forum, Road Safety Report 2020 Hungary. OECD, 2020. Online: https://www.itf-oecd.org/sites/default/files/hungary-road-safety.pdf

10. Komaco, Weather Observation. Online: http://komaconvis.com/stech3_weatherobserv

11. Központi Statisztikai Hivatal, Közlekedési baleseti statisztikai évkönyv 2015. Budapest, KSH, 2016. Online: https://www.ksh.hu/docs/hun/xftp/idoszaki/baleset/baleset15.pdf

12. Központi Statisztikai Hivatal, Magyarországon első alkalommal forgalomba helyezett új és használt közúti gépjárművek száma járműnemenként. KSH, 2019. Online: https://www.ksh.hu/docs/hun/xstadat/xstadat_eves/i_ode007.html

13. S. T. Kral et al., „Innovative Strategies for Observations in the Arctic Atmospheric Boundary Layer (ISOBAR) – The Hailuoto 2017 Campaign,” Atmosphere, Vol. 9, No. 7. 268. pp. 1–29. 2018. Online: https://doi.org/10.3390/atmos9070268

14. T. Litman, Autonomous Vehicle Implementation Predictions. Implications for Transport Planning. Victoria Transport Policy Institute, 2021. Online: https://www.vtpi.org/avip.pdf

15. M. Pearson, Certification and Standards – Manned and Unmanned Flight. Vertical Flight Society, April 22, 2020. Online: https://vtol.org/files/dmfile/20200422---marilynpearson---faa---weather-standards.pdf

16. Meteomatics, Meteodrones-Meteobase. Online: https://www.meteomatics.com/en/meteodrones-meteob ase/

17. National Aeronautics and Space Administration, „NASA-led Airborne Mission Studies Storm Intensification in Northern Hemisphere,” NASA, 2017. augusztus 15. Online: https://www.nasa.gov/centers/armstrong/features/airborne-mission-studies-northern-hemisphere.html

18. A. Perrels, V. Nurmi, P. Nurmi, „Weather Service Chain Analysis (WSCA) – An Approach For Appraisal of the Social-Economic Benefits of Improvements in Weather Services,” In 16th International Road Weather Conference, SIRWEC 2012, May 2012. pp. 1–8.

19. R. Hranac et al., Empirical Studies on Traffic Flow in Inclement Weather. Washington, Contract nr. FHWA-HOP-07-073, Federal Highway Administration, 2006. Online: https://vtechworks.lib.vt.edu/bitstream/handle/10919/55110/weatherempirical.pdf

20. J-P. Rodrigue, The Geography of Transport Systems. New York, Routledge, 2020. Online: https://doi.org/10.4324/9780429346323

21. Rohács J., Rohács D., „Total Impact Evaluation of Transportation Systems,” Transport, Vol. 35, No. 2. pp. 193–202. 2020. Online: https://doi.org/10.3846/transport.2020.12640

22. S. Mayer et al., „Atmospheric Profiling with the UAS SUMO: A New Perspective for the Evaluation of Fine-Scale

Atmospheric Models,” Meteorology and Atmospheric Physics, Vol. 116, No. 1. pp. 15–26. 2012. Online: https://doi.org/10.1007/s00703-010-0063-2

23. Szegedi Cs., Dombai F., Csirmaz K., Németh P., Országos meteorológia szolgálat időjárási radarhálózatának mérései. Budapest, Országos Meteorológiai Szolgálat, 2014.

24. V. A. Korolkov et al., „Autonomous Weather Stations for Unmanned Aerial Vehicles. Preliminary Results of Measurements of Meteorological Profiles,” IOP Conf. Series: Earth and Environmental Science, Vol. 211, p. 012069. 2018. Online: https://doi.org/10.1088/1755-1315/211/1/012069

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