Accurate positioning of vehicles and pedestrians is crucial for enhancing road safety. In this paper, we propose and compare two implementations based on Unscented Kalman Filter (UKF) and Particle Filter (PF) to perform trajectory estimation with sensor fusion. For the latter, a novel soft map-matching technique is applied on top of a PF. The main benefit of our method is the possibility of detecting reliably critical situations, like vehicles skidding off the road. Moreover, we can reduce the positioning error by 45% w.r.t. prior art approaches. Our solution can be implemented as a cloud service in the 5G mobile radio network.

Robust and Flexible Tracking of Vehicles Exploiting Soft Map-Matching and Data Fusion

M. Mizmizi;L. Reggiani
2018-01-01

Abstract

Accurate positioning of vehicles and pedestrians is crucial for enhancing road safety. In this paper, we propose and compare two implementations based on Unscented Kalman Filter (UKF) and Particle Filter (PF) to perform trajectory estimation with sensor fusion. For the latter, a novel soft map-matching technique is applied on top of a PF. The main benefit of our method is the possibility of detecting reliably critical situations, like vehicles skidding off the road. Moreover, we can reduce the positioning error by 45% w.r.t. prior art approaches. Our solution can be implemented as a cloud service in the 5G mobile radio network.
Proceedings of 88th Vehicular Technology Conference (VTC-Fall)
978-1-5386-6358-5
5G, V2X, autonomous driving, Kalman Filter, Particle Filter, road safety, map-matching
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1085555
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