INLANE - Galileo Village

Donostia/San Sebastián, 
  • Booth: 2913

Low cost lane-level navigation with GNSS and Computer Vision

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.

The objective is to develop a new generation, low-cost, lane-level, precise turn-by-turn navigation application through the fusion of EGNSS and Computer Vision technology.  This will enable a new generation of enhanced mapping information with real-time updating based on crowdsourcing techniques. The resulting lane-level vehicle positioning will bring navigation and traffic management systems to a new level of detail and effectiveness.

inLane develop a low-cost module based on E- GNSS (GPS/GLONASS/Galileo), EGNOS/ADAS, Inertial Measurement Units (IMU) and computer vision. This precise positioning module will also have an interface for linking with smartphone-like platforms. inLane also defines system architecture and specifications to ensure the fusion of all necessary components for the deployment in the next generation Automotive Computers, Smartphones and Tablets, as well as  After Market Solutions

To address these needs, inLane develops new, computer vision based, road modelling (lane modelling) and traffic signal identification, to be geo-located according to data provided by the inLane precise positioning module. This will allow to create a new generation of enhanced maps, updated with crowdsourcing information, for lane-level operations.

inLane targets at reaching the required 5 cm accuracy related to absolute and in-lane location, thanks to the use and development of complex fusion and hybridization algorithms for GNSS, IMU, Map and Computer Vision signal

The Lane-level navigation components will be integrated into mobile phone and on-board automotive computing platforms to enable quick prototyping and validation. The resulting technologies will be market ready with immediate application to ADAS, Autonomous Vehicles and Robotics.

Expected impact:

  • Promote adoption of European GNSS applications
  • Contribute in setting common standards
  • Foster continued European leadership in automotive
  • Advances in the state of the art of computer vision and machine learning
  • Leverage new EuroNCAP active safety standards and North American and Asia Car Manufactures dependence on these standards.