How Lyft Creates Hyper-Accurate Maps From Open-Source Maps and Real-Time Data

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How Lyft Creates Hyper-Accurate Maps from Open-Source Maps and Real-Time Data

At Lyft, our novel driver localization algorithm detects map errors to create a hyper-accurate map from OpenStreetMap (OSM) and real-time data. We have fixed thousands of map errors in OSM in bustling urban areas. Later in the post, we share a sample of the detected map errors in Minneapolis with the OSM Community to improve the quality of the map.

[...] Our internal map of the road network is based on OSM, which has been built and improved over the years by the open source community. More recently, larger organizations (such as Apple, Microsoft, Mapbox, Mapillary, Facebook, Uber, Grab, Lyft, etc.)^1 have also worked to improve the map. Akin to Wikipedia as an open-source encyclopedia, OSM as an open-source map may contain missing or erroneous data for several possible reasons. Old roads may have never been mapped, new roads may not have been mapped yet, previously closed roads may be reopened, roads may be digitally vandalized, buildings may be non-existent, turn restrictions may be erroneous, road directions may be incorrectly labeled, and so forth. As OSM is a source for our basemap, we need to monitor its quality and accuracy. Upon detecting map errors in OSM, we work with our Data Curation Team to fix them in OSM. This can be done using our proprietary data.

Before discussing map error detection, it is necessary to have an understanding of what map-matching is. At Lyft, we geo-localize drivers from the sensors embedded in their smartphones. This includes a GPS receiver that receives sparse (due to battery constraints) and often noisy (due to urban canyons) locations.

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