Enhanced perception for autonomous vehicles at obstructed intersections: an implementation of vehicle to infrastructure (V2I) collaboration
In urban intersections, the sensory capabilities of autonomous vehicles (AVs) are often hindered by visual obstructions, posing significant challenges to their robust and safe operation. This paper presents an implementation study focused on enhancing the safety and robustness of Connected Automated...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2024
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| Online Access: | https://hdl.handle.net/10356/174872 |
| _version_ | 1811688275839025152 |
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| author | Mo, Yanghui Vijay, Roshan Rufus, Raphael de Boer, Niels Kim, Jungdae Yu, Minsang |
| author2 | Energy Research Institute @ NTU (ERI@N) |
| author_facet | Energy Research Institute @ NTU (ERI@N) Mo, Yanghui Vijay, Roshan Rufus, Raphael de Boer, Niels Kim, Jungdae Yu, Minsang |
| author_sort | Mo, Yanghui |
| collection | NTU |
| description | In urban intersections, the sensory capabilities of autonomous vehicles (AVs) are often hindered by visual obstructions, posing significant challenges to their robust and safe operation. This paper presents an implementation study focused on enhancing the safety and robustness of Connected Automated Vehicles (CAVs) in scenarios with occluded visibility at urban intersections. A novel LiDAR Infrastructure System is established for roadside sensing, combined with Baidu Apollo's Automated Driving System (ADS) and Cohda Wireless V2X communication hardware, and an integrated platform is established for roadside perception enhancement in autonomous driving. The field tests were conducted at the Singapore CETRAN (Centre of Excellence for Testing & Research of Autonomous Vehicles-NTU) autonomous vehicle test track, with the communication protocol adhering to SAE J2735 V2X communication standards. Communication latency and packet delivery ratio were analyzed as the evaluation metrics. The test results showed that the system can help CAV detect obstacles in advance under urban occluded scenarios. |
| first_indexed | 2024-10-01T05:29:37Z |
| format | Journal Article |
| id | ntu-10356/174872 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:29:37Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1748722024-04-16T15:39:50Z Enhanced perception for autonomous vehicles at obstructed intersections: an implementation of vehicle to infrastructure (V2I) collaboration Mo, Yanghui Vijay, Roshan Rufus, Raphael de Boer, Niels Kim, Jungdae Yu, Minsang Energy Research Institute @ NTU (ERI@N) Engineering Vehicle–infrastructure cooperative perception Roadside sensing system In urban intersections, the sensory capabilities of autonomous vehicles (AVs) are often hindered by visual obstructions, posing significant challenges to their robust and safe operation. This paper presents an implementation study focused on enhancing the safety and robustness of Connected Automated Vehicles (CAVs) in scenarios with occluded visibility at urban intersections. A novel LiDAR Infrastructure System is established for roadside sensing, combined with Baidu Apollo's Automated Driving System (ADS) and Cohda Wireless V2X communication hardware, and an integrated platform is established for roadside perception enhancement in autonomous driving. The field tests were conducted at the Singapore CETRAN (Centre of Excellence for Testing & Research of Autonomous Vehicles-NTU) autonomous vehicle test track, with the communication protocol adhering to SAE J2735 V2X communication standards. Communication latency and packet delivery ratio were analyzed as the evaluation metrics. The test results showed that the system can help CAV detect obstacles in advance under urban occluded scenarios. Agency for Science, Technology and Research (A*STAR) Published version The work presented in this paper was supported by the RIE2020 Advanced Manufacturing and Engineering (AME) Industry Alignment Fund–PrePositioning (IAF-PP) (Grant No.: A19D6a0053) Grant from Agency Science Technology & Research (A*Star) to undertake the Project titled “NextGeneration V2X Network Architecture and Ecosystem for Smart Mobility”. 2024-04-15T02:29:34Z 2024-04-15T02:29:34Z 2024 Journal Article Mo, Y., Vijay, R., Rufus, R., de Boer, N., Kim, J. & Yu, M. (2024). Enhanced perception for autonomous vehicles at obstructed intersections: an implementation of vehicle to infrastructure (V2I) collaboration. Sensors, 24(3), 24030936-. https://dx.doi.org/10.3390/s24030936 1424-8220 https://hdl.handle.net/10356/174872 10.3390/s24030936 38339653 2-s2.0-85184656968 3 24 24030936 en A19D6a0053 Sensors © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). application/pdf |
| spellingShingle | Engineering Vehicle–infrastructure cooperative perception Roadside sensing system Mo, Yanghui Vijay, Roshan Rufus, Raphael de Boer, Niels Kim, Jungdae Yu, Minsang Enhanced perception for autonomous vehicles at obstructed intersections: an implementation of vehicle to infrastructure (V2I) collaboration |
| title | Enhanced perception for autonomous vehicles at obstructed intersections: an implementation of vehicle to infrastructure (V2I) collaboration |
| title_full | Enhanced perception for autonomous vehicles at obstructed intersections: an implementation of vehicle to infrastructure (V2I) collaboration |
| title_fullStr | Enhanced perception for autonomous vehicles at obstructed intersections: an implementation of vehicle to infrastructure (V2I) collaboration |
| title_full_unstemmed | Enhanced perception for autonomous vehicles at obstructed intersections: an implementation of vehicle to infrastructure (V2I) collaboration |
| title_short | Enhanced perception for autonomous vehicles at obstructed intersections: an implementation of vehicle to infrastructure (V2I) collaboration |
| title_sort | enhanced perception for autonomous vehicles at obstructed intersections an implementation of vehicle to infrastructure v2i collaboration |
| topic | Engineering Vehicle–infrastructure cooperative perception Roadside sensing system |
| url | https://hdl.handle.net/10356/174872 |
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