Next Article in Journal
1-Point RANSAC UKF with Inverse Covariance Intersection for Fault Tolerance
Next Article in Special Issue
Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review
Previous Article in Journal
Evaluation of Three Different Approaches for Automated Time Delay Estimation for Distributed Sensor Systems of Electric Vehicles
Previous Article in Special Issue
Real-Time Hybrid Multi-Sensor Fusion Framework for Perception in Autonomous Vehicles
Open AccessArticle

Managing Localization Uncertainty to Handle Semantic Lane Information from Geo-Referenced Maps in Evidential Occupancy Grids

1
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 / Revised: 25 December 2019 / Accepted: 31 December 2019 / 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. View Full-Text
Keywords: evidential occupancy grid; uncertainty; lane grid; prior map; semantic evidential occupancy grid; uncertainty; lane grid; prior map; semantic
Show Figures

Figure 1

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 Style

Yu, Chunlei; Cherfaoui, Veronique; Bonnifait, Philippe; Yang, Dian-ge. 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

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop