Live Road Condition Assessment with Internal Vehicle Sensors

Eyal Levenberg, Asmus Skar*, Shahrzad M. Pour, Ekkart Kindler, Matteo Pettinari, Milena Bajic, Tommy S. Alstrom, Uwe Schlotz

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review


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.
Original languageEnglish
Book seriesTransportation Research Record
Issue number10
Number of pages11
Publication statusPublished - 2021


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