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Cooperative Longitudinal Positioning at Intersections Using DSRC

: Zhang, Haiyang; Schäufele, Bernd; Hark, Johann Nikolai; Sawade, Oliver; Radusch, Ilja


Institute of Electrical and Electronics Engineers -IEEE-:
IEEE 88th Vehicular Technology Conference, VTC-Fall 2018. Proceedings : Chicago, IL, 27-30 August 2018
Piscataway, NJ: IEEE, 2018
ISBN: 978-1-5386-6358-5
ISBN: 978-1-5386-6357-8
ISBN: 978-1-5386-6359-2
5 pp.
Vehicular Technology Conference (VTC Fall) <88, 2018, Chicago/Ill.>
Conference Paper
Fraunhofer FOKUS ()

Cooperative driver assistance systems (CoDAS) allow automated vehicles to perform cooperative driving maneuvers using vehicle to vehicle communication (V2V). For planning, negotiation and execution, these functions require precise localization of the vehicle itself and of other vehicles in the surroundings. Most vehicles today are equipped with Global Navigation Satellite System receivers (GNSS). However GNSS are prone to errors up to several meters, far exceeding CoDAS requirements. Yet, most of these errors are similar for vehicles in close proximity as they are caused by the satellite clock or atmospheric effects. In this paper we show how these characteristics can be used to improve absolute position of connected cars fusing GNSS and map data. Map matching is used to determine the GNSS offset of vehicles that share their position through V2V. The lateral error correction of a map matched vehicle that drives in perpendicular direction to the ego vehicle at an intersection is converted to a longitudinal error correction for the ego vehicle. We present an evaluation of this approach in simulation environment and real-world test drives using a LiDAR-based ground truth. We show that the cooperative positioning can reduce the mean GPS error below one meter.