Application of Multi-Source Remote Sensing Technology in Smart Transportation

Siyi He; Kuiliang Jiang; Jinghui Li

doi:10.62051/thzdyg80

Authors

Siyi He
Kuiliang Jiang
Jinghui Li

DOI:

https://doi.org/10.62051/thzdyg80

Keywords:

Remote Sense; Intelligent Transportation; Faster R-CNN; LiDAR.

Abstract

Under the background of deepening urbanization, the rapid growth of traffic flow poses a great challenge to the traffic monitoring system. However, the development of intelligent transportation systems (ITS) integrates advanced information technology and multiple remote sensing technologies, thus opening up a new path for efficient urban traffic management. This study explores in depth the applications of unmanned aerial vehicle remote sensing technology, LiDAR technology, and airborne remote sensing technology in autonomous driving technology, traffic flow monitoring, road condition assessment, and vehicle environment perception. This paper analyzes that UAV remote sensing technology can provide a wide field of vision and accurate environmental awareness for autonomous vehicles by carrying high-resolution cameras and sophisticated radar devices, so as to enhance the response ability of autonomous vehicle to complex traffic scenes. This article also analyzes the application of LiDAR technology in achieving precise vehicle positioning and navigation, as well as road information collection. This technology utilizes laser pulses to generate high-precision 3D point cloud maps, providing accurate distance measurement and environmental modeling for autonomous vehicles. The airborne remote sensing technology can be applied in road condition assessment, accident detection and response, environmental monitoring, and traffic congestion analysis due to its high-resolution monitoring capability, wide coverage, and fast response characteristics. Multi-source remote sensing technology plays a vital role in improving the efficiency of traffic monitoring, optimizing traffic flow management, and promoting the advancement of autonomous driving technology.

Downloads

Download data is not yet available.

References

[1] Tan Q, Ling J, Hu J, Qin X, Hu J. Vehicle detection in high resolution satellite remote sensing images based on deep learning. IEEE Access. 2020, 8:153394 - 153402. doi: 10.1109/ACCESS.2020.3017894.

[2] Girshick R. Proceedings of the IEEE International Conference on Computer Vision (ICCV). 2015, 1440 - 1448.

[3] Yin Z, Tang Y. Analysis of traffic flow in urban area for satellite video. IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020 Jul 26 - 31, Waikoloa, HI, USA. IEEE, 2020. p. 2898 - 2901. doi: 10.1109/IGARSS39084.2020.9324725.

[4] Chang Y, Wang S, Zhou Y, Wang L, Wang F. A novel method of evaluating highway traffic prosperity based on nighttime light remote sensing. Remote Sensing. 2020, 12 (1): 102. doi: 10.3390/rs12010102.

[5] Li J, Xu Z, Fu L, Zhou X, Yu H. Domain adaptation from daytime to nighttime: A situation-sensitive vehicle detection and traffic flow parameter estimation framework. Transportation Research Part C: Emerging Technologies. 2021, 124: 102946.

[6] Liu S, Yu B, Tang J, Zhu Q. Invited: Towards fully intelligent transportation through infrastructure-vehicle cooperative autonomous driving: Challenges and opportunities. 2021 58th ACM/IEEE Design Automation Conference (DAC), 2021 Dec 5-9, San Francisco, CA, USA. IEEE, 2021. p. 1323 - 1326. doi: 10.1109/DAC18074.2021.9586317.

[7] Wallar A, Araki B, Chang R, Alonso-Mora J, Rus D. Foresight: Remote sensing for autonomous vehicles using a small unmanned aerial vehicle. In: Hutter M, Siegwart R, editors. Field and Service Robotics. Springer Proceedings in Advanced Robotics, vol 5. Cham: Springer, 2018. doi: 10.1007/978-3-319-67361-5_38.

[8] Madany YM, Elaziz DA, Elkrim WA. Design and analysis of compact ultra-wideband inverted F-L microstrip patch antenna for intelligent transportation communication systems. 2012 15th International Symposium on Antenna Technology and Applied Electromagnetics, 2012 Jun 25-28, Toulouse, France. IEEE, 2012. p. 1-4. doi: 10.1109/ANTEM.2012.6262362.

[9] Dong R, Hu M, Cui T, et al. A mathematical modeling approach for optimal parking space selection and path planning in autonomous parking systems with UAV-assisted TOPSIS entropy weight method. PREPRINT (Version 1) available at Research Square, 2023 Nov 28. doi: 10.21203/rs.3.rs-3639234/v1.

[10] Lee KW. Extraction of road information based on high resolution UAV image processing for autonomous driving support. Journal of the Korea Academia-Industrial Cooperation Society. 2017, 18 (8): 355 - 360.

[11] Li Y, Ibanez-Guzman J. Lidar for autonomous driving: The principles, challenges, and trends for automotive lidar and perception systems. IEEE Signal Processing Magazine. 2020, 37 (4): 50 - 61. doi: 10.1109/MSP.2020.2973615.

[12] Roriz R, Cabral J, Gomes T. Automotive LiDAR technology: A survey. IEEE Transactions on Intelligent Transportation Systems. 2022, 23 (7): 6282 - 6297. doi: 10.1109/TITS.2021.3086804.

[13] Reinartz P, Lachaise M, Schmeer E, et al. Traffic monitoring with serial images from airborne cameras. ISPRS Journal of Photogrammetry and Remote Sensing. 2006, 61 (3): 149 - 158.

[14] Palubinskas G, Kurz F, Reinartz P. Detection of traffic congestion in optical remote sensing imagery. 2008 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2008, 2008 Jul 6-11, Boston, MA, USA. IEEE, 2009. p. II - 426 – II - 429.

[15] Yao W, Hinz S, Stilla U. Extraction and motion estimation of vehicles in single-pass airborne LiDAR data towards urban traffic analysis. ISPRS Journal of Photogrammetry and Remote Sensing. 2011, 66 (3): 260 - 271.

[16] Yuan H, Yang J, Li X, Ma S. Congestion analysis based on remote sensing images. In: Yuan H, Geng J, Liu C, Bian F, Surapunt T, editors. Geo-Spatial Knowledge and Intelligence: GSKI 2017. Communications in Computer and Information Science, vol 848. Singapore: Springer, 2018. p. 397 - 410. doi: 10.1007/978-981-13-0893-2_37.