Map Matching and Lanes Number Estimation with Openstreetmap

Kasmi, Abderrahim; Denis, Dieumet; Aufrère, Romuald; Chapuis, Roland

doi:10.1109/itsc.2018.8569840

Cited by 15 publications

(19 citation statements)

References 13 publications

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“…In a more specific way, the latest developments use the OpenStreetMap (OSM) database to perform the MM. In our previous work [12], a multi-criteria map-matching algorithm based on multiple probabilistic criteria has been introduced. Nevertheless, the road map topology has not been properly operated in the MM process, which consists of one of the contributions of this paper.…”

Section: Localization On a Mapmentioning

confidence: 99%

“…In this section, we present our road-level localization algorithm using OSM datasets. As shown in Figure 2 the proposed module is an upgrade of our work presented in [12]. Indeed, an HMM is added to robustify the proposed Map-Matching.…”

Section: Road-level Localization (Rll)mentioning

confidence: 99%

“…Thus, the discrimination stage consists of the exclusion of segments which are not compatible with the current vehicle and the road network topology configuration. To do so, we introduced three discrimination criteria [12]:…”

Section: Discrimination Stagementioning

confidence: 99%

“…Consequently, a selection criterion must be introduced to determine the correct Way. Based on equation (1) three probabilistic criteria [12] were developed:…”

Section: Selection Of the Correct Waymentioning

confidence: 99%

“…i = 1, ..., n Roi with n Roi the number of Region Of Interest (ROI) for each lane marking. As mentioned in [12], the OSM map does not provide information about the accuracy of its data. However, it is well known that OSM is a collaborative project in which volunteers provide the geospatial data.…”

Section: Ego-lane Level Localization (Ell)mentioning

confidence: 99%

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End-to-End Probabilistic Ego-Vehicle Localization Framework

Kasmi

Laconte

Aufrère

et al. 2021

IEEE Trans. Intell. Veh.

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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.

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Section: Localization On a Mapmentioning

confidence: 99%

Section: Road-level Localization (Rll)mentioning

confidence: 99%

Section: Discrimination Stagementioning

confidence: 99%

“…Consequently, a selection criterion must be introduced to determine the correct Way. Based on equation (1) three probabilistic criteria [12] were developed:…”

Section: Selection Of the Correct Waymentioning

confidence: 99%

Section: Ego-lane Level Localization (Ell)mentioning

confidence: 99%

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End-to-End Probabilistic Ego-Vehicle Localization Framework

Kasmi

Laconte

Aufrère

et al. 2021

IEEE Trans. Intell. Veh.

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Lane Detection in Autonomous Vehicles: A Systematic Review

et al. 2023

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One of the essential systems in autonomous vehicles for ensuring a secure circumstance for drivers and passengers is the Advanced Driver Assistance System (ADAS). Adaptive Cruise Control, Automatic Braking/Steer Away, Lane-Keeping System, Blind Spot Assist, Lane Departure Warning System, and Lane Detection are examples of ADAS. Lane detection displays information specific to the geometrical features of lane line structures to the vehicle's intelligent system to show the position of lane markings. This article reviews the methods employed for lane detection in an autonomous vehicle. A systematic literature review (SLR) has been carried out to analyze the most delicate approach to detecting the road lane for the benefit of the automation industry. One hundred and two publications from well-known databases were chosen for this review. The trend was discovered after thoroughly examining the selected articles on the method implemented for detecting the road lane from 2018 until 2021. The selected literature used various methods, with the input dataset being one of two types: self-collected or acquired from an online public dataset. In the meantime, the methodologies include geometric modeling and traditional methods, while AI includes deep learning and machine learning. The use of deep learning has been increasingly researched throughout the last four years. Some studies used stand-alone deep learning implementations for lane detection problems. Meanwhile, some research focuses on merging deep learning with other machine learning techniques and classical methodologies. Recent advancements imply that attention mechanism has become a popular combined strategy with deep learning methods. The use of deep algorithms in conjunction with other techniques showed promising outcomes. This research aims to provide a complete overview of the literature on lane detection methods, highlighting which approaches are currently being researched and the performance of existing state-of-the-art techniques. Also, the paper covered the equipment used to collect the dataset for the training process and the dataset used for network training, validation, and testing. This review yields a valuable foundation on lane detection techniques, challenges, and opportunities and supports new research works in this automation field. For further study, it is suggested to put more effort into accuracy improvement, increased speed performance, and more challenging work on various extreme conditions in detecting the road lane.

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An Information Driven Approach For Ego-Lane Detection Using Lidar And OpenStreetMap

Kasmi

Laconte

Aufrère

et al. 2020

2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV)

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Localizing the vehicle in its lane is a critical task for any autonomous vehicle. By and large, this task is carried out primarily through the identification of ego-lane markings. In recent years, ego-lane marking detection systems have been the subject of various research topics, using several inputs data such as camera or lidar sensors. Lately, the current trend is to use high accurate maps (HD maps) that provide accurate information about the road environment. However, these maps suffer from their availability and their price tag. An alternative is the use of affordable low-accurate maps. Yet, there is relatively little work on it. In this paper, we propose an information-driven approach that takes into account inaccurate prior geometry of the road from OpenStreetMap (OSM) to perform ego-lane marking detection using solely a lidar. The two major novelties presented in this paper are the use of the OSM datasets as prior for the road geometry, which reduces the research area in the lidar space, and the information-driven approach, which guarantees that the outcome of the detection is coherent to the road geometry. The robustness of the proposed method is proven on real datasets and statistical metrics are used to highlight our method's efficiency.

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Map Matching and Lanes Number Estimation with Openstreetmap

Cited by 15 publications

References 13 publications

End-to-End Probabilistic Ego-Vehicle Localization Framework

End-to-End Probabilistic Ego-Vehicle Localization Framework

Lane Detection in Autonomous Vehicles: A Systematic Review

An Information Driven Approach For Ego-Lane Detection Using Lidar And OpenStreetMap

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