Skip to Main content Skip to Navigation
Journal articles

End-to-End Probabilistic Ego-Vehicle Localization Framework

Abstract : Locating the vehicle in its road is a critical part of any autonomous vehicle system and has been subject to different research topics. In most works presented in the literature, ego-localization is split into three parts: Road level-localization consisting in the road on which the vehicle travels, Lane level localization which is the lane on which the vehicle travels, and Ego lane level localization being the lateral position of the vehicle in the ego-lane. For each part, several researches have been conducted. However, the relationship between the different parts has not been taken into consideration. Through this work, an end-to-end ego-localization framework is introduced with two main novelties. The first one is the proposition of a complete solution that tackles every part of the ego-localization. The second one lies in the information-driven approach used. Indeed, we use prior about the road structure from a digital map in order to reduce the space complexity for the recognition process. Besides, several fusion framework techniques based on Bayesian Network and Hidden Markov Model are elaborated leading to an ego-localization method that is, to a large extent, robust to erroneous sensor data. The robustness of the proposed method is proven on different datasets in varying scenarios.
Document type :
Journal articles
Complete list of metadata
Contributor : Abderrahim Kasmi Connect in order to contact the contributor
Submitted on : Wednesday, December 9, 2020 - 7:09:11 PM
Last modification on : Wednesday, February 24, 2021 - 4:16:03 PM
Long-term archiving on: : Wednesday, March 10, 2021 - 8:01:44 PM


Files produced by the author(s)


Distributed under a Creative Commons Attribution 4.0 International License



Abderrahim Kasmi, Johann Laconte, Romuald Aufrère, Dieumet Denis, Roland Chapuis. End-to-End Probabilistic Ego-Vehicle Localization Framework. IEEE Transactions on Intelligent Vehicles, Institute of Electrical and Electronics Engineers, 2020, pp.1-1. ⟨10.1109/TIV.2020.3017256⟩. ⟨hal-03049396⟩



Record views


Files downloads