Cooperative Vehicle Localization Systems
Summary
Cooperative vehicle localisation systems integrate information from multiple vehicles and infrastructure to enhance positioning accuracy, integrity and availability for connected and automated vehicles. By exchanging data such as GNSS measurements, LiDAR point clouds, inertial readings and radio-based ranging, vehicles overcome limitations of standalone sensors in urban canyons, tunnels and GNSS-denied environments. Architectures range from centralised schemes, where roadside units or cloud servers fuse incoming data, to decentralised networks employing peer-to-peer protocols and distributed optimisation. Techniques include differential GNSS, real-time kinematic corrections, simultaneous localisation and mapping, ultra-wideband ranging and sensor fusion via Kalman filters or particle filters. Recent advances harness graph-based neural networks for inter-vehicle data association and implicit cooperative positioning algorithms, yielding sub-metre to centimetre-level accuracy. Such systems promise to improve safety, traffic efficiency and autonomous navigation, with applications spanning platooning, urban transit monitoring and emergency response.
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Cooperative Vehicle Localization Systems publication trend
The graph below shows the total number of articles in cooperative vehicle localization systems across all publications each year (not limited to Nature Index journals).
Technical terms
Global Navigation Satellite System (GNSS): A suite of satellite constellations providing real-time geolocation and time information worldwide.
Simultaneous Localisation and Mapping (SLAM): A method by which vehicles concurrently build a map of an environment and estimate their position within it.
Vehicle-to-Vehicle (V2V) communication: Direct data exchange between vehicles to share positioning and sensor information.
Vehicle-to-Infrastructure (V2I) communication: Data exchange between vehicles and roadside units or centralised nodes for cooperative services.
LiDAR (Light Detection and Ranging): A sensor that measures distances by illuminating targets with laser light and analysing the reflected pulses.
Ultra-Wideband (UWB): A radio technology employing short-duration pulses for high-precision ranging and localisation.
Real-Time Kinematic (RTK): A GNSS enhancement technique that provides centimetre-level positioning by correcting satellite signal errors in real time.
Inertial Navigation System (INS): A self-contained system using accelerometers and gyroscopes to estimate position and orientation without external references.
Message-Passing Neural Network (MPNN): A graph-based deep-learning model that iteratively exchanges information between connected nodes for data association and inference.
References
- Deep Learning-Based Cooperative LiDAR Sensing for Improved Vehicle Positioning. IEEE Transactions on Signal Processing (2024).
- Experimental Assessment of UWB and Vision-Based Car Cooperative Positioning System. Remote Sensing (2021).
- Cooperative GNSS-RTK Ambiguity Resolution with GNSS, INS, and LiDAR Data for Connected Vehicles. Remote Sensing (2020).
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