LiRA book

This book summarizes the activities and results obtained from the LiRA project. It is a structured collection of contributions from all four project partners. The book is written with an overall aim of providing the community with a unified text about the topic of road condition assessment using data collected from built-in sensors in conventional passenger cars. It provides all the relevant information needed to develop a similar concept including i) technical specifications and limitations of the used technol ogy, ii) data validation and calibration procedures, iii) design of the data infrastructure and database including challenges, iv) development of the different models used to predict road measures. Ultimately, it is hoped that the book will promote and highlight new business opportunities providing key information to the relevant stakeholders.



Asmus Skar, Anders M. Vestergaard, Thea Brüsch, Shahrzad Pour, Ekkart Kindler, Tommy Sonne Alstrøm, Uwe Schlotz, Jakob Elsborg Larsen, Matteo Pettinari (2023). LiRA-CD: An open-source dataset for road condition modelling and research. Data in Brief, Volume 49.

Asmus Skar, Natasja Ringsing Nielsen, Matteo Pettinari and Eyal Levenberg (2023), Towards infrastructure energy labelling utilizing data from a connected fleet of electric vehicles. Transportation Research Procedia, Volume 72, Pages 2676-2683

Skar A, Vestergaard A, Pour SM, Pettinari M. (2023). Internet-of-Things (IoT) Platform for Road Energy Efficiency Monitoring. Sensors, volume 23(5):2756.

Asmus Skar and Eyal Levenberg (2023), Road Profile Inversion from In-Vehicle Accelerometers. Journal of Transportation Engineering, Part B: Pavements, Volume 150, Issue 1.

Eyal Levenberg, Asmus Skar, Shahrzad M. Pour, Ekkart Kindler, Matteo Pettinari, Milena Bajic, Tommy Alstrøm and Uwe Schlotz (2021), Live Road Condition Assessment with Internal Vehicle Sensors. Transportation Research Record, 2675(10), 1442–1452.

Zhao Du, Asmus Skar, Matteo Pettinari and Zhu, Xingyi (2023), Pavement friction evaluation based on vehicle dynamics and vision data using a multi-feature fusion network. Transportation Research Record, 2677(11), 219-236.

Eyal Levenberg (2023), Estimating the tire-pavement grip potential from vehicle vibrations. Transportation Research Record, 2677(7), 237-248.

Shahrzad M. Pour, Niels Skov Dujardin, Matteo Pettinari (2022), “Data collection”, Book chapter in “PIARC Literature review use of big data for road condition monitoring”, accepted

Milena Bajic, Shahrzad M Pour, Asmus Skar, Matteo Pettinari, Eyal Levenberg, Tommy Sonne Alstrøm, Road Roughness Estimation Using Machine Learning. (2021) arXiv preprint arXiv:2107.01199

LiRA webinar

Student projects within LiRA

Master (MSc) thesis

Markus Berthold, Live Road Assessment based on modern cars: A prototypical geographic information system for road maintenance planning, MSc Thesis 01/2020, DTU Compute,

Thomas Bech Madsen, LiRA: A Microservice Architecture for Collecting, Processing, Accessing and Visualizating Geospatial Data MSc Thesis 01/2021, DTU Compute,

Mike Glisby, LiRA PMS: A Pavement Management System Exploiting Online Road Condition Data in an Efficient Way, MSc Thesis 07/2021, DTU Compute,

Andreas Soelberg, A Method for Integrating and Organizing Software Projects Comprised of Independent Subprojects and Data Sources, MSc Thesis 02/2022, DTU Compute,

Jonas Gottlieb Svendsen, Johan Bloch Madsen, Road condition assessment using deep learning, MSc Thesis, 2021, DTU Compute.

Daniel Alejandro Campos Rivera, Map-matching GPS data to roads in Denmark by using car sensor information, MSc Thesis, 2021, DTU Compute.

Nicola Pleth, Road Damage Detection using Deep Learning with car sensor data, MSc Thesis, 2022, DTU Compute.

Jun Chen, A Scalable Architecture for Storing, Processing, Retrieving and Visualizing Road Condition Data, MSc Thesis 07/2022, DTU Compute.

Aayush Dhakal, An architectural framework for deploying machine learning to production, MSc Thesis 01/2022, DTU Compute,

Mads Finnerup Nielsen, LiRA: A Software Architecture for Deploying Predictive Machine Learning Models, MSc Thesis, In progress (expected in 02/2023), DTU Compute.

Bachelor (BSc) thesis

Jonathan Drud Bendsen, LiRA Map: A Cloud-based Geo-Information System for Road Maintenance, Bachelor thesis, BSc Project 05/2020, DTU Compute,

Alexander Faarup Christensen, A Flexible Framework for Generic Data Collection from Vehicles, BSc Thesis 06/2020, DTU Compute,

Daniel Kuzin, A Web Application for Validating Georeferenced Car Data by Domain Experts, BSc Thesis 06/2021, DTU Compute,

Martin Daniel Nielsen, Mads Dyrved Møller, A Method for Determining the Road Gradient Using Data Collected from Vehicles, BSc Thesis 08/2021, DTU Compute,

Muse Ali, Elastic Scaling of a Modularized Microservice Architecture: A Use Case on a Big Data Pipeline for Road Data Collection, BSc Project 02/2022, DTU Compute,

Nathan Maire, Machine Learning in LiRA: Aggregation and Visualization of Road Condition Data, BSc Thesis 06/2022, DTU Compute,

Sergi Doce, Understanding Road Condition and Car Data: A Flexible Framework for the Visualization of Spatio-Temporal Data, BSc Thesis 06/2022, DTU Compute,