Low Cost GNSS and Computer Vision Fusion for Accurate Lane Level Navigation and Enhanced Automatic Map Generation (inLane)
The objective of inLane is to deliver lane-level information to an in-vehicle navigation system giving drivers the
opportunity to select the optimal road lane, even in the case of dense urban and extra-urban traffic. This will be
realised through the fusion of EGNSS and Computer Vision technology. inLane will reduce the risks associated with
last-moment lane-change manoeuvres and enable a new generation of enhanced mapping information based on crowd sourcing.
Lane-level positioning and map matching are some of the biggest challenges for navigation systems. Although vehicle
telematics provide services with positioning requirements fulfilled by low-cost GNSS receivers, more complex road and
driver assistance applications are increasingly been deployed, due to the growing demand. These include lane-level
information as well as lane-level navigation and prioritised alerts depending on the scenario composition (traffic
sign, navigation instructions, ADAS instructions). These applications need a more accurate and reliable positioning
subsystem. A good example of these new requirements can be witnessed in the increasing interest in navigation at
lane-level, with applications such as enhanced driver awareness, intelligent speed alert and simple lane allocation.
As well as the accuracy of positioning data being a big driver, there is also a question around the adaptability of
navigation systems to these applications. This depends firstly on the availability of an accurate common reference
for positioning (an enhanced map) and secondly, on the level of the provided pose estimation (integrity). However,
neither the current road maps nor the traditional integrity parameters seem to be well suited for these purposes.
Delivering lane-level information to an in-vehicle navigation system and combining this with the opportunity for
vehicles to exchange information between themselves, will give drivers the opportunity to select the optimal road
lane, even in dense traffic in urban and extra-urban areas. Every driver will be able to choose the appropriate lane
and will to be able to reduce the risks associate with last-moment lane-change manoeuvres. inLane proposes new
generation, lowcost, lane-level, precise turn-by-turn navigation applications through the fusion of EGNSS and Computer
Vision technology. This will enable a new generation of enhanced mapping information based on crowdsourcing.
Vicomtech – IK4, Spain (Coordinator)
ERTICO – ITS Europe, Belgium
Honda Research Institute Europe GmbH, Germany
Intel Corporation, Germany
TeleConsult Austria GmbH, Austria
TomTom International BV, Netherlands
Technische Universiteit Eindhoven, Netherlands
Automobil Club Assistencia SA, Spain
Institut Municipal D'Informática de Barcelona, Spain
This project has received funding from the European GNSS Agency under the European Union's Horizon 2020 innovation
programme under grant agreement No 687458.
inLane project website