Leveraging Control Theory to Facilitate UAV Application for CAV Deployment

Recent studies have espoused the benefits of connectivity among ground vehicles, which include proactive safety management, savings in fuel consumption, efficiency of traffic mobility, and reduction in emissions. Connectivity makes it possible to acquire data on the traffic streams (and any disruptions thereto), as well as threats and opportunities associated with the weather conditions, pedestrians, and the non-roadway environment. With such data, the ground vehicles can make more informed decisions that reduce delay and yield the attendant benefits associated with safety, mobility, emissions, and energy use. In quests to identify additional potential sources of data, researchers have identified the opportunity offered by Unmanned Aerial Vehicles (UAVs) in this regard. It has been found that UAVs can acquire and transit aerial data to ground vehicles and other end users quickly and cost-effectively. First, camera-equipped UAVs acquire visual information about the terrain they are monitoring. Secondly, UAVs provide greater efficiency and convenience compared to surveillance cameras with fixed camera angle, scale and view. Thirdly, if UAVs are equipped with Vehicle-to-Everything (V2X) communication technologies, they can lend another dimension of communication in the Connected and Autonomous vehicle (CAV) data environment. Previous studies have used microscopic traffic simulation to investigate and exploit the potential benefits and use cases of a CAV-UAV connected networks. In the proposed study, the research team intends to use real life (but, smaller scale) simulation of both UAV and CAVs. The team intends to show how the traffic and roadway environment information can be collected by the connected UAVs and disseminated to the CAVs below. The team will then assess the performance of the CAVs in fuel economy and traffic mobility vis-à-vis the baseline case of no UAV communication and the case where only connected UAVs were present. The team shall do this for a number of scenarios involving traffic and roadway conditions, and they shall identify the conditions under which the UAV application is most beneficial to CAV deployment. In addition, recognizing that the benefits of UAVs are not just for information delivery to the ground CAVs, the team shall show how control theory can be used by the UAV to help provide prescriptions for safe and efficient movement of the CAVSs. The proposed study will also demonstrate the efficacy of a DSRC based communication architecture between the connected UAV and the ground CAVs, and examine the issues related to package loss in the data transfer, reliability of the DSRC communication, and communication ranges, latency, and scalability to real life deployment. The practical benefits of the proposed products are numerous. A reliable UAV-CAV data domain can help the road agency carry out traffic safety risk assessment and vehicle trajectory monitoring. From the control perspective, it can also help establish optimal operational maneuvers for CAVs (including weaving and lane-change) and trajectory planning.

• Record URL:
  https://ccat.umtri.umich.edu/research/purdue/leveraging-control-theory-to-facilitate-uav-application-for-cav-deployment/

Language

• English

Project

• Status: Completed
• Funding: $200000
• Contract Numbers:

  69A3551747105

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Center for Connected and Automated Transportation

  University of Michigan Transportation Research Institute
  Ann Arbor, MI  United States  48109
• Project Managers:

  Tucker-Thomas, Dawn

  Bezzina, Debra

• Performing Organizations:

  Purdue University, Lyles School of Civil Engineering

  550 Stadium Mall Drive
  West Lafayette, IN  United States  47907
• Principal Investigators:

  Chen, Sikai

  Mou, Shaoshuai

• Start Date: 20220401
• Expected Completion Date: 20240229
• Actual Completion Date: 20240311
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Connected vehicles; Data collection; Dedicated short range communications; Unmanned aircraft systems; Wireless communication systems
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01846014
• Record Type: Research project
• Source Agency: Center for Connected and Automated Transportation
• Contract Numbers: 69A3551747105
• Files: UTC, RIP
• Created Date: May 21 2022 8:08AM