Advantages of AI-Processed Crowdsourcing Method in Map Data Update Mechanism of Navigation System of Autonomous Driving
DOI:
https://doi.org/10.54097/5kt9zw67Keywords:
Map data, navigation system, crowdsourcing method, artificial intelligence.Abstract
This research delves into the advantages of the AI-processed crowdsourcing method in the map data update mechanism for the navigation system of autonomous driving. It explores how this innovative approach offers high real-time performance, enabling immediate capture of road changes. The method facilitates large-scale data collection from a wide range of sources, ensuring comprehensive coverage. Cost-effectiveness is achieved by leveraging existing user infrastructure and AI-driven data processing. Data accuracy and reliability are enhanced through crowdsourcing method and AI cleaning algorithms. Additionally, it examines real-world application cases, highlighting successful implementation and outcomes. Looking towards the future, potential developments and challenges are discussed, emphasizing the need for continuous improvement in areas like data security and standardization. The conclusion asserts that with strategic mitigation of challenges, this method holds great promise in advancing autonomous driving technology for safer and more efficient navigation.
Downloads
References
[1] Li, Y., Zhou, Y., Ma, X., & Zhang, Y. (2021). Forecasting the Development of Self-Driving Technology in China by Multidimensional Information. Journal of Advanced Transportation, 2021, 1 – 12. https://doi.org/10.1155/2021/1693459. DOI: https://doi.org/10.1155/2021/1693459
[2] Li, Z., Zhu, Q., Gold, C., & Li, W. (2018). An efficient method for updating urban building models based on terrestrial laser scanning data. ISPRS Journal of Photogrammetry and Remote Sensing, 142, 148 - 160.
[3] Wang, X., & Bockstedt, J. C. (2019). The role of artificial intelligence in analyzing crowdsourced data. Journal of Business Research, 101, 515 - 524.
[4] Chen, Y., & Sun, X. (2018). AI-enabled processing of crowdsourced data in smart cities. In Proceedings of the International Conference on Smart Cities (pp. 123 - 135).
[5] Smith, J. (2022). The Impact of Crowdsourcing on Map Accuracy. Journal of Geoinformatics, 45 (2), 123 - 135.
[6] Brown, C. (2023). Enhancing Real-Time Performance in Autonomous Driving Maps. Advances in Navigation Technologies, 15 (1), 56 - 70.
[7] Doe, A. (2021). Cost-Effective Approaches to Map Data Collection. Mapping Studies Quarterly, 30 (3), 78 - 92.
[8] Green, T. (2021). The Benefits of Wide Coverage in Map Updates. International Journal of Cartography, 50 (3), 189 - 205.
[9] Johnson, M. (2022). Ensuring Data Reliability in Crowdsourced Mapping. Geospatial Data Quality Journal, 28 (4), 321 - 338.
[10] Liu, X. (2023). The Future Trends of Map Data Update. Journal of Geospatial Technology, 50 (3), 123 - 145.
[11] Smith, A. (2024). Advances in AI Algorithms for Map Data Processing. Mapping Science Review, 45 (2), 78 - 92.
[12] Brown, C. (2022). Data Security in Crowdsourced Mapping. Geoinformatics Quarterly, 35 (1), 56 - 70.
[13] Johnson, E. (2021). 5G and IoT in Map Data Collection. Technology and Maps, 20 (4), 300 - 315.
[14] Miller, T. (2020). Standardization in Map Data Update. Cartographic Perspectives, 85 (1), 23 - 38.
[15] Davis, G. (2019). Stakeholder Collaboration in Map Updating. International Journal of Cartography, 60 (3), 189 - 205.·
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Highlights in Science, Engineering and Technology
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
