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http://hdl.handle.net/10397/96618
Title: | Pavement marking incorporated with binary code for accurate localization of autonomous vehicles | Authors: | Wang, Y Wang, Y Ho, WHI Sheng, W Chen, L |
Issue Date: | Nov-2022 | Source: | IEEE transactions on intelligent transportation systems, Nov. 2022, v. 23, no. 11, p. 22290-22300 | Abstract: | Accurate localization is a critically important issue for autonomous vehicles as it is closely related to the safety and efficiency of autonomous driving. However, current technologies for autonomous vehicle localization face many challenges. To provide accurate and robust localization services to autonomous vehicles, we propose a novel solution by employing a newly designed pavement marking. This marking operates on color contrast, temperature contrast, and binary code with some special features. We also trained and customized an object detector based on a deep learning model: YOLOv5, and integrated it with the decoding algorithm. The localization system is capable of running at a steady frame rate of more than 50 FPS. Road trials up to 80 km/h were conducted, and satisfactory results confirmed the feasibility and robustness of the localization system. Specifically, with a common onboard camera, more than four continuous frames can be detected and decoded correctly when the speed is slower than 30 km/h. At least one frame can be detected and decoded correctly at a higher speed (i.e., 30– 50 km/h). With a high-speed camera, more than 18 frames can be detected and decoded even at 80 km/h. The findings suggest that the specially designed road marking and associated algorithms can provide a viable and economical option for accurate localization of autonomous vehicles. The performance of the system has potentials for further improvement by using better hardware such as faster CPUs, GPUs, and thermal imaging techniques. | Keywords: | Roads Location awareness Autonomous vehicles Global Positioning System Image color analysis Cameras Wireless fidelity |
Publisher: | Institute of Electrical and Electronics Engineers | Journal: | IEEE transactions on intelligent transportation systems | ISSN: | 1524-9050 | EISSN: | 1558-0016 | DOI: | 10.1109/TITS.2022.3173656 | Rights: | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The following publication Y. Wang, Y. Wang, I. W. -H. Ho, W. Sheng and L. Chen, "Pavement Marking Incorporated With Binary Code for Accurate Localization of Autonomous Vehicles," in IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 11, pp. 22290-22300, Nov. 2022, is available at https://dx.doi.org/10.1109/TITS.2022.3173656 |
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Wang_Localization_Autonomous_Vehicles.pdf | Pre-Published version | 10.01 MB | Adobe PDF | View/Open |
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