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Autonomous drive systems are a dynamically developed sector of the automotive industry. The key problem in such technological solutions is to provide a reliable navigation system, which is typically based on high-definition (HD) maps supporting the identification of the position of a maneuvering vehicle. HD maps should include possibly up-to-date and detailed information on traffic lanes and on the traffic rules and regulations on such lanes. An effective development of an HD map should be based on the geodetic measurement methods, which ensure efficient and accurate acquisition of spatial data. This article presents the results of an experiment consisting in the manipulation of data obtained with the use of the mobile laser scanning method and further in employing this data in the development of an HD map in an open-source environment. The applied measurement technology and the processing method allowed data of high resolution (frequently above 1000 points per m2 ) and of high accuracy (3D accuracy down to less than 5 cm). The obtained data were processed in the Vector Map Builder environment (which is accessible from the level of an internet browser) and the final product - HD map was created in the Lanelet2 open-source environment. The above-described experiments allowed two main conclusions. Most importantly, they demonstrate the importance of planning and performing in-field mobile laser scanning measurements. They also point to the important role of the human analyst who needs to manually vectorize the key elements of road infrastructure and to define traffic rules.
Czasopismo
Rocznik
Tom
Strony
87--103
Opis fizyczny
Bibliogr. 24 poz., il., tab.
Twórcy
autor
- Wroclaw University of Science and Technology, Wrocław, Poland
autor
- Wroclaw University of Science and Technology, Wrocław, Poland
autor
- Wroclaw University of Science and Technology, Wrocław, Poland
Bibliografia
- 1. ASAM 2024. https://www.asam.net/standards/detail/opendrive/, access date 25.02.2024.
- 2. Bao, Z, Hossain, S, Lang, H, & Lin, X 2023. A review of high-definition map creation methods for autonomous driving. Engineering Applications of Artificial Intelligence 122, 106125.
- 3. Barroso, D G, Yang, Y, Machado, F A, & Emadi, A 2021. Electrified automotive propulsion systems: state-of-the-art review. IEEE Transactions on Transportation Electrification 8(2), 2898- 2914.
- 4. Dokic, J, Müller B, Meyer G 2015. European roadmap smart systems for automated driving. European Technology Platform on Smart Systems Integration 39.
- 5. Gran, C W 2019. HD-maps in autonomous driving, M.S. thesis, Dept. Comp. Sci., Norwegian Univ. of Sci. and Tech., Norway.
- 6. HERE 2024. https://www.here.com/platform/HD-live-map, access date 25.02.2024.
- 7. Ilci, V & Toth, C 2020. High definition 3D map creation using GNSS/IMU/LiDAR sensor integration to support autonomous vehicle navigation. Sensors 20(3), 899.
- 8. Liu, R, Wang, J & Zhang, B 2020. High definition map for automated driving: Overview and analysis. The Journal of Navigation 73(2), 324-341.
- 9. Liu, Y, et al. 2022. "VectorMapNet: End-to-end Vectorized HD Map Learning." arXiv preprint arXiv:2206.08920.
- 10. Marks, P 2014. Hop in, I'm flying. New Scientist 224(2992), 19-20.
- 11. Massow, K, Kwella, B, Pfeifer, N, Häusler, F, Pontow, J, Radusch, I, ... & Haueis, M. 2016. Deriving HD maps for highly automated driving from vehicular probe data. IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), 1745-1752.
- 12. McAllister, R et al. 2017. Concrete problems for autonomous vehicle safety: Advantages of Bayesian deep learning. International Joint Conferences on Artificial Intelligence.
- 13. Nang, H X 2021. A comparison between 3d high-definition maps created by photogrammetry and by laser scanning applied for an autonomous vehicle. Vietnam Journal of Science and Technology 59 (3) (2021) 402-411.
- 14. Natan, O and Jun, M 2022. DeepIPC: Deeply Integrated Perception and Control for Mobile Robot in Real Environments. arXiv preprint arXiv:2207.09934.
- 15. NVIDIA 2024. https://www.nvidia.com/en-us/self-driving-cars/hd-mapping/, access date 25.02.2024.
- 16. Pardi, T 2021. Prospects and contradictions of the electrification of the European automotive industry: the role of European Union policy. International Journal of Automotive Technology and Management 21(3), 162-179.
- 17. Poggenhans, F, Pauls, J H, Janosovits, J, Orf, S, Naumann, M, Kuhnt, F, & Mayr, M 2018. Lanelet2: A high-definition map framework for the future of automated driving.21st international conference on intelligent transportation systems (ITSC), 1672-1679.
- 18. Seif, H G & Hu, X 2016. Autonomous driving in the iCity-HD maps as a key challenge of the automotive industry. Engineering 2(2), 159-162.
- 19. Stayton, E L 2015. Driverless dreams: technological narratives and the shape of the automated car. Diss. Massachusetts Institute of Technology.
- 20. TomTom 2024. https://www.tomtom.com/newsroom/behind-the-map/how-we-make-our-hd maps/, access date 25.02.2024.
- 21. Toschi, I et al. 2015. Accuracy evaluation of a mobile mapping system with advanced statistical methods. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 40.5.
- 22. Wong, K, Gu, Y, & Kamijo, S 2020. Mapping for autonomous driving: Opportunities and challenges. IEEE Intelligent Transportation Systems Magazine 13(1), 91-106.
- 23. Zhang, F, Shi, W, Chen, M, Huang, W, & Liu, X 2023. Open HD map service model: an interoperable high-Definition map data model for autonomous driving. International Journal of Digital Earth 16(1), 2089-2110.
- 24. Ziegler, J et al. 2014. Making bertha drive - an autonomous journey on a historic route. IEEE Intelligent transportation systems magazine 6.2, 8-20.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-22013f32-01bf-4376-ae83-670b77a7eb2d