Infrastructure Enhancements for CAV Navigation
This report presents a review of the main sensors used in connected and autonomous vehicles (CAVs). Radar, ultrasonic sensors, a global positioning system, radio-frequency identification, lidar, cameras, inertial measurement units, and capacitive–proximity sensors were detailed, listing their working principles, advantages, and disadvantages. Based on the review, potential measures to address their drawbacks and enhance control and guidance of CAV were proposed. The measures mainly consist of modification of electromagnetic, optical, and thermal properties of infrastructure materials in order for vehicles to communicate with the infrastructure.
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Supplemental Notes:
- This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program. Supporting datasets available at: https://doi.org/10.4231/2EEQ-3R20; https://rosap.ntl.bts.gov/view/dot/61994
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Corporate Authors:
University of Illinois, Urbana-Champaign
Department of Civil and Environmental Engineering
205 North Mathews Avenue
Urbana, IL United States 61801-2352Center for Connected and Automated Transportation
University of Michigan Transportation Research Institute
Ann Arbor, MI United States 48109Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590 -
Authors:
- Dahal, Sachindra
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0000-0002-1118-9756
- Hernandez, Jaime
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0000-0002-9665-0408
- Roesler, Jeffery
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0000-0001-6194-269X
- Publication Date: 2018-11
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; References; Tables;
- Pagination: 36p
Subject/Index Terms
- TRT Terms: Autonomous vehicles; Connected vehicles; Infrastructure; Mobile communication systems; Sensors
- Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01835296
- Record Type: Publication
- Report/Paper Numbers: ICT-20-008, UILU-ENG-2020-2008
- Contract Numbers: 69A3551747105
- Files: UTC, NTL, TRIS, ATRI, USDOT
- Created Date: Feb 7 2022 9:41AM