Leveraging UAV Capabilities for Vehicle Tracking and Collision Risk Assessment at Road Intersections

Shuya Zong, Sikai Chen, Majed Alinizzi and Samuel Labi
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Shuya Zong: Center for Connected and Automated Transportation (CCAT), Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA
Sikai Chen: Center for Connected and Automated Transportation (CCAT), Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA
Majed Alinizzi: Department of Civil Engineering, College of Engineering, Qassim University, Buraydah 51452, Saudi Arabia
Samuel Labi: Center for Connected and Automated Transportation (CCAT), Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA

Sustainability, 2022, vol. 14, issue 7, 1-20

Abstract: 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.

Keywords: unmanned aerial vehicles; risk assessment; trajectory tracking; transportation safety; deep learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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