Live Road Condition Assessment with Internal Vehicle Sensors

Modern cars are equipped with many sensors that measure information about the vehicle and its surroundings. These measurements are therefore related to the ride-surface conditions over which the vehicle is passing. The paper commences by outlining a four-component vision for performing road condition evaluation based on in-vehicle sensor readings and subsequent feeding of pavement management systems (PMSs) with live condition information. This is expected to enrich the functionalities of PMSs, and ultimately lead to improved maintenance and repair decisions. Next the LiRA (Live Road Assessment) project—an ongoing proof-of-concept attempt to realize the vision components—is presented. The project elements and software architecture are described in detail, listing any assumptions, compromises, and challenges. LiRA involves a fleet of 400 electric cars operating in Copenhagen, both within the city streets and nearby highways. Sensor data collection is performed with a customized Internet of Things (IoT) device installed in the cars. Data processing and structuring involve new software tools for quality control, spatio-temporal interpolation, and map matching. A hybrid approach, combining machine learning models with physical (mechanics-based) models, is currently being applied to convert data into relevant information for PMSs. Based on the experience thus far with LiRA, the vision actualization is deemed achievable, workable, and up-scalable.

• Record URL:
  https://doi.org/10.1177/03611981211016852
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/03611981
• Supplemental Notes:
  • Eyal Levenberg https://orcid.org/0000-0003-1188-8458 © National Academy of Sciences: Transportation Research Board 2021.
• Authors:
  • Levenberg, Eyal
  • 0000-0003-1188-8458
  • Skar, Asmus
  • 0000-0003-3176-791X
  • Pour, Shahrzad M
  • 0000-0002-2585-2360
  • Kindler, Ekkart
  • 0000-0003-3895-6297
  • Pettinari, Matteo
  • Bajic, Milena
  • 0000-0002-3055-2549
  • Alstrøm, Tommy S
  • 0000-0003-0941-3146
  • Schlotz, Uwe
  • 0000-0001-6932-7739
• Publication Date: 2021-10

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 1442-1452
• Serial:
  • Transportation Research Record: Journal of the Transportation Research Board
  • Volume: 2675
  • Issue Number: 10
  • Publisher: Sage Publications, Incorporated
  • ISSN: 0361-1981
  • EISSN: 2169-4052
  • Serial URL: http://journals.sagepub.com/home/trr

Subject/Index Terms

• TRT Terms: Computer network protocols; Data analysis; Data communications; In vehicle sensors; Internet; Machine learning; Monitoring; Pavement management systems; Real time information
• Subject Areas: Data and Information Technology; Highways; Pavements; Vehicles and Equipment;

Filing Info

• Accession Number: 01763550
• Record Type: Publication
• Report/Paper Numbers: TRBAM-21-03128
• Files: TRIS, TRB, ATRI
• Created Date: Feb 4 2021 10:54AM