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With the on-going disruption of the transportation industry and rapid advancement in ITS technologies; emerging smart cities, navigation systems and autonomous transportation, the need for highly accurate geospatial localization has never been more crucial. These technologies demand that we have more granular location information of vehicles not just on a road, but to a specific lane on the road.
This presentation will give a pedagogical style summary and overview of some of the on-going research work at HERE Technologies and how we have pushed the state-of-the-art in lane-localization of noisy GPS probe data using novel Machine Learning Algorithms and how some of these innovations is being applied to power new products for real-time traffic, routing and navigation systems, maps for autonomous vehicles, incidents and safety services.
An example of such product is Split Lane Traffic (SLT)
Split Lane Traffic (SLT) detects divergent traffic speeds at highway junctions with exit ramps. It is the first traffic product that provides lane maneuver guidance information to drivers based on lane-level traffic conditions ahead thereby giving better navigation experience and a more accurate routing and ETA.
James Fowe is a Principal Research Engineer in the Connected Vehicle Services division at HERE Technologies. As part of the Advanced Engineering Team, he leads the design and implementation of Mathematical models and Machine Learning Algorithms for location intelligence as related to real-time traffic flow, location-based AdTech, safety services and HD Maps for Autonomous driving. More recently James has been pioneering cutting edge research focused on deriving lane-level granularity from noisy GPS probe data. This includes lane-level map-matching, lane-level traffic, lane-closures and lane-connectivity in high definition maps. James has been involved with ITS research for over a decade and has invented several key technologies in the field with over 50 US patents filed and a few academic research paper publications.
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Fowe, James, "Deriving Lane-level Insight from GPS Data: Innovations for Traffic & Autonomous Driving" (2019). TREC Friday Seminar Series. 166.