A robust methodology for dynamic proximity sensing of vehicles overtaking micromobility devices in a noisy environment
The safety of cyclists, e-scooters, and micromobility devices in urban environments remains a critical concern in sustainable urban planning. A primary factor affecting this safety is the lateral passing distance (LPD) or dynamic proximity of motor vehicles overtaking micromobility riders. Minimum p...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
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2024
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| Online Access: | https://hdl.handle.net/10356/178887 |
| _version_ | 1811680565665988608 |
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| author | Yap, Wuihee Paudel, Milan Yap, Fook Fah Vahdati, Nader Shiryayev, Oleg |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Yap, Wuihee Paudel, Milan Yap, Fook Fah Vahdati, Nader Shiryayev, Oleg |
| author_sort | Yap, Wuihee |
| collection | NTU |
| description | The safety of cyclists, e-scooters, and micromobility devices in urban environments remains a critical concern in sustainable urban planning. A primary factor affecting this safety is the lateral passing distance (LPD) or dynamic proximity of motor vehicles overtaking micromobility riders. Minimum passing distance laws, where motorists are required to maintain a minimum distance of 1.5 m when passing a cyclist, are difficult to enforce due to the difficulty in determining the exact distance between a moving vehicle and a cyclist. Existing systems reported in the literature are invariably used for research and require manual intervention to record passing vehicles. Further, due to the dynamic and noisy environment on the road, the collected data also need to be manually post-processed to remove errors and false positives, thus making such systems impractical for use by cyclists. This study aims to address these two concerns by providing an automated and robust framework, integrating a low-cost, small single-board computer with a range sensor and a camera, to measure and analyze vehicle–cyclist passing distance and speed. Preliminary deployments in Singapore have demonstrated the system’s efficacy in capturing high-resolution data under varied traffic conditions. Our setup, using a Raspberry Pi 4, LiDAR distance sensor, a small camera, and an automated data clustering technique, had a high success rate for correctly identifying the number of close vehicle passes for distances between 1 and 1.5 m. The insights garnered from this integrated setup promise not only a deeper understanding of interactions between motor vehicles and micromobility devices, but also a roadmap for data-driven urban safety interventions. |
| first_indexed | 2024-10-01T03:27:04Z |
| format | Journal Article |
| id | ntu-10356/178887 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:27:04Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1788872024-07-13T16:48:07Z A robust methodology for dynamic proximity sensing of vehicles overtaking micromobility devices in a noisy environment Yap, Wuihee Paudel, Milan Yap, Fook Fah Vahdati, Nader Shiryayev, Oleg School of Mechanical and Aerospace Engineering Transport Research Centre Engineering Cyclist safety Distance measurement The safety of cyclists, e-scooters, and micromobility devices in urban environments remains a critical concern in sustainable urban planning. A primary factor affecting this safety is the lateral passing distance (LPD) or dynamic proximity of motor vehicles overtaking micromobility riders. Minimum passing distance laws, where motorists are required to maintain a minimum distance of 1.5 m when passing a cyclist, are difficult to enforce due to the difficulty in determining the exact distance between a moving vehicle and a cyclist. Existing systems reported in the literature are invariably used for research and require manual intervention to record passing vehicles. Further, due to the dynamic and noisy environment on the road, the collected data also need to be manually post-processed to remove errors and false positives, thus making such systems impractical for use by cyclists. This study aims to address these two concerns by providing an automated and robust framework, integrating a low-cost, small single-board computer with a range sensor and a camera, to measure and analyze vehicle–cyclist passing distance and speed. Preliminary deployments in Singapore have demonstrated the system’s efficacy in capturing high-resolution data under varied traffic conditions. Our setup, using a Raspberry Pi 4, LiDAR distance sensor, a small camera, and an automated data clustering technique, had a high success rate for correctly identifying the number of close vehicle passes for distances between 1 and 1.5 m. The insights garnered from this integrated setup promise not only a deeper understanding of interactions between motor vehicles and micromobility devices, but also a roadmap for data-driven urban safety interventions. Published version 2024-07-10T02:07:05Z 2024-07-10T02:07:05Z 2024 Journal Article Yap, W., Paudel, M., Yap, F. F., Vahdati, N. & Shiryayev, O. (2024). A robust methodology for dynamic proximity sensing of vehicles overtaking micromobility devices in a noisy environment. Applied Sciences, 14(9), 3602-. https://dx.doi.org/10.3390/app14093602 2076-3417 https://hdl.handle.net/10356/178887 10.3390/app14093602 2-s2.0-85192789417 9 14 3602 en Applied Sciences © 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 Cyclist safety Distance measurement Yap, Wuihee Paudel, Milan Yap, Fook Fah Vahdati, Nader Shiryayev, Oleg A robust methodology for dynamic proximity sensing of vehicles overtaking micromobility devices in a noisy environment |
| title | A robust methodology for dynamic proximity sensing of vehicles overtaking micromobility devices in a noisy environment |
| title_full | A robust methodology for dynamic proximity sensing of vehicles overtaking micromobility devices in a noisy environment |
| title_fullStr | A robust methodology for dynamic proximity sensing of vehicles overtaking micromobility devices in a noisy environment |
| title_full_unstemmed | A robust methodology for dynamic proximity sensing of vehicles overtaking micromobility devices in a noisy environment |
| title_short | A robust methodology for dynamic proximity sensing of vehicles overtaking micromobility devices in a noisy environment |
| title_sort | robust methodology for dynamic proximity sensing of vehicles overtaking micromobility devices in a noisy environment |
| topic | Engineering Cyclist safety Distance measurement |
| url | https://hdl.handle.net/10356/178887 |
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