Leveraging UAV Capabilities for Vehicle Tracking and Collision Risk Assessment at Road Intersections
Transportation agencies continue to pursue crash reduction. Initiatives include the design of safer facilities, promotion of safe behaviors, and assessments of collision risk as a precursor to the identification of proactive countermeasures. Collision risk assessment includes reliable prediction of vehicle trajectories. Unfortunately, in using traditional tracking equipment, such prediction can be impaired by occlusion. It has been suggested in recent literature that unmanned aerial vehicles (UAVs) can be deployed to address this issue successfully, given their wide visual field and movement flexibility. This paper presents a methodology that integrates UAVs to track the movement of road users and to assess potential collisions at intersections. The proposed methodology includes an existing deep-learning-based algorithm to identify road users, extract trajectories, and calculate collision risk. The methodology was applied using a case study, and the results show that the methodology can provide beneficial information for the purpose of measuring and analyzing the infrastructure performance. Based on vehicle movements it observes, the UAV can communicate its collision risk to each vehicle so that the vehicle can undertake proactive driving decisions. Finally, the proposed framework can serve as a valuable tool for urban road agencies to develop measures to reduce crash risks.
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- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/20711050
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Supplemental Notes:
- Copyright: © 2022 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/).
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Authors:
- Zong, Shuya
- Chen, Sikai
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0000-0002-5931-5619
- Alinizzi, Majed
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0000-0003-4237-8522
- Labi, Samuel
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0000-0001-9830-2071
- Publication Date: 2022-4
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; Photos; References; Tables;
- Pagination: 4034
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Serial:
- Sustainability
- Volume: 14
- Issue Number: 7
- Publisher: MDPI AG
- ISSN: 2071-1050
- Serial URL: http://www.mdpi.com/journal/sustainability
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Publication flags:
Open Access (libre)
Subject/Index Terms
- TRT Terms: Drones; Intersections; Risk assessment; Tracking systems; Traffic crashes; Vehicle trajectories
- Subject Areas: Highways; Planning and Forecasting; Safety and Human Factors; Vehicles and Equipment;
Filing Info
- Accession Number: 01914407
- Record Type: Publication
- Files: NTL, TRIS
- Created Date: Apr 15 2024 8:37AM