The future of urban mobility is Automated Driving Systems (ADS). We are not outlying from witnessing self-driving cars on public roads. With a market growth projection of $1.64 billion in 2021 to $11.03 billion in 2028 [siz] ADS demonstrates the enormous potential of research activities. The mapping platform is critical in the development of ADS, but due to the high cost of authoritative geospatial data sets such as HERE maps, it becomes necessary to investigate the use of open source mapping entities in ADS development. With the introduction of OSM as a mapping entity, this thesis aims to investigate the use of OpenStreetMap (OSM) in the development of Automated Driving Systems. The thesis will propose a solution for mining Operational Design Domains (ODD) from OSM’s database using appropriate data mining technique and determining its usability. The software should mine and analyze ODDs, and the results will be available to ADS engineers on a web portal for L4 automated vehicle performance analysis. By end of this thesis, the research proposes an effective and appropriate data mining mechanism for extracting ODD data from the OSM database. We investigate into operational domain properties, including physical road network, provides an answer to the usability of OpenStreetMap’s extracted ODD parameters in the context of ADS development. The verification of ODDs is performed in order to assess the data quality of OSM. Finally, the presented solution determines whether the OSM data model’s attributes are complete, whether there is consistency in usability results across cities, and a relationship between data density and usability results. As a result, the conclusion regarding OSM’s use in ADS is determined.