Managing Localization Uncertainty to Handle Semantic Lane Information from Geo-Referenced Maps in Evidential Occupancy Grids †
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State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, 10084 Beijing, China
2
Sorbonne Universités, Université de Technologie de Compiègne, CNRS Heudiasyc UMR 7253, 60203 Compiegne, France
*
Author to whom correspondence should be addressed.
†
This paper is an extended version of our paper published in Yu, C.; Cherfaoui, V.; Bonnifait, P. Semantic evidential lane grids with prior maps for autonomous navigation. In Proceeding of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil, 1–4 November 2016.
Sensors 2020, 20(2), 352; https://doi.org/10.3390/s20020352
Received: 6 September 2019
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Revised: 25 December 2019
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Accepted: 31 December 2019
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Published: 8 January 2020
(This article belongs to the Special Issue Sensor Data Fusion for Autonomous and Connected Driving)
Occupancy grid is a popular environment model that is widely applied for autonomous navigation of mobile robots. This model encodes obstacle information into the grid cells as a reference of the space state. However, when navigating on roads, the planning module of an autonomous vehicle needs to have semantic understanding of the scene, especially concerning the accessibility of the driving space. This paper presents a grid-based evidential approach for modeling semantic road space by taking advantage of a prior map that contains lane-level information. Road rules are encoded in the grid for semantic understanding. Our approach focuses on dealing with the localization uncertainty, which is a key issue, while parsing information from the prior map. Readings from an exteroceptive sensor are as well integrated in the grid to provide real-time obstacle information. All the information is managed in an evidential framework based on Dempster–Shafer theory. Real road results are reported with qualitative evaluation and quantitative analysis of the constructed grids to show the performance and the behavior of the method for real-time application.
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Keywords:
evidential occupancy grid; uncertainty; lane grid; prior map; semantic
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Externally hosted supplementary file 1
Link: https://youtu.be/0F078KJkSRo
Description: An illustrative video of our method.
MDPI and ACS Style
Yu, C.; Cherfaoui, V.; Bonnifait, P.; Yang, D.-g. Managing Localization Uncertainty to Handle Semantic Lane Information from Geo-Referenced Maps in Evidential Occupancy Grids. Sensors 2020, 20, 352. https://doi.org/10.3390/s20020352
AMA Style
Yu C, Cherfaoui V, Bonnifait P, Yang D-g. Managing Localization Uncertainty to Handle Semantic Lane Information from Geo-Referenced Maps in Evidential Occupancy Grids. Sensors. 2020; 20(2):352. https://doi.org/10.3390/s20020352
Chicago/Turabian StyleYu, Chunlei, Veronique Cherfaoui, Philippe Bonnifait, and Dian-ge Yang. 2020. "Managing Localization Uncertainty to Handle Semantic Lane Information from Geo-Referenced Maps in Evidential Occupancy Grids" Sensors 20, no. 2: 352. https://doi.org/10.3390/s20020352
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