Special Issue "Mapping for Autonomous Vehicles"

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (15 November 2017)

Special Issue Editor

Guest Editor
Prof. Dr. Arpad Barsi

Department of Photogrammetry and Geoinformatics, Budapest University of Technology and Economics, H-1111 Budapest. Muegyetem rkp. 3, Hungary
Website | E-Mail
Phone: +36-1-463 1186
Fax: +36-1-463 3084
Interests: GIS data capturing methods; navigation; network analysis; intelligent transportation systems (ITS); artificial intelligence; autonomous vehicles; digital image processing; photogrammetry

Special Issue Information

Dear Colleagues,

In recent years, more and more research is dealing with autonomous and self-driving vehicles; and the driven kilometers are also growing rapidly. The technological development stimulates the demand for better maps, where, not only the accuracy and resolution have to be increased, but also a new role for maps emerges. The newly produced maps will not only help to find an optimal route to a destination, but must support the positioning procedure as well. In some situations (e.g., in parking houses) the map-based positioning can efficiently improve the accuracy, while a higher safety level is enabled due to redundant data acquisition.

This Special Issue seeks contributions exploring the recent developments and future potential in high definition mapping, 3D modeling of urban and rural environment, big map data storage and update, real time map databases and supporting traffic safety.

Prof. Dr. Arpad Barsi
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (4 papers)

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Research

Open AccessArticle A Distance-Adaptive Refueling Recommendation Algorithm for Self-Driving Travel
ISPRS Int. J. Geo-Inf. 2018, 7(3), 94; https://doi.org/10.3390/ijgi7030094
Received: 12 December 2017 / Revised: 2 March 2018 / Accepted: 7 March 2018 / Published: 12 March 2018
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Abstract
Taking the maximum vehicle driving distance, the distances from gas stations, the route length, and the number of refueling gas stations as the decision conditions, recommendation rules and an early refueling service warning mechanism for gas stations along a self-driving travel route were
[...] Read more.
Taking the maximum vehicle driving distance, the distances from gas stations, the route length, and the number of refueling gas stations as the decision conditions, recommendation rules and an early refueling service warning mechanism for gas stations along a self-driving travel route were constructed by using the algorithm presented in this research, based on the spatial clustering characteristics of gas stations and the urgency of refueling. Meanwhile, by combining ArcEngine and Matlab capabilities, a scenario simulation system of refueling for self-driving travel was developed by using c#.net in order to validate and test the accuracy and applicability of the algorithm. A total of nine testing schemes with four simulation scenarios were designed and executed using this algorithm, and all of the simulation results were consistent with expectations. The refueling recommendation algorithm proposed in this study can automatically adapt to changes in the route length of self-driving travel, the maximum driving distance of the vehicle, and the distance from gas stations, which could provide variable refueling recommendation strategies according to differing gas station layouts along the route. Therefore, the results of this study could provide a scientific reference for the reasonable planning and timely supply of vehicle refueling during self-driving travel. Full article
(This article belongs to the Special Issue Mapping for Autonomous Vehicles)
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Open AccessArticle Extraction of Road Intersections from GPS Traces Based on the Dominant Orientations of Roads
ISPRS Int. J. Geo-Inf. 2017, 6(12), 403; https://doi.org/10.3390/ijgi6120403
Received: 25 October 2017 / Revised: 22 November 2017 / Accepted: 7 December 2017 / Published: 10 December 2017
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Abstract
Many studies have used Global Navigation Satellite System (GNSS) traces to successfully extract segments of road networks because such data can be rapidly updated at a low cost. However, most studies have not focused on extracting intersections, which are indispensable parts of road
[...] Read more.
Many studies have used Global Navigation Satellite System (GNSS) traces to successfully extract segments of road networks because such data can be rapidly updated at a low cost. However, most studies have not focused on extracting intersections, which are indispensable parts of road networks in terms of connectivity. However, extracted intersections often present unsatisfactory precision and misleading connectivity. This study proposes a novel method for extracting road intersections from Global Position System (GPS) trace points and for capturing intersections with better accuracy. The key to improving the geometric accuracy of intersections is to identify the dominant orientations of road segments around intersections, merge similar orientations and maintain independent conflicting orientations. Extracting intersections by aligning the dominant orientations can largely reduce location offsets and road distortions. Experiments are performed to demonstrate the increased accuracy and connectivity of extracted road intersections by the proposed method. Full article
(This article belongs to the Special Issue Mapping for Autonomous Vehicles)
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Open AccessArticle Development of a Change Detection Method with Low-Performance Point Cloud Data for Updating Three-Dimensional Road Maps
ISPRS Int. J. Geo-Inf. 2017, 6(12), 398; https://doi.org/10.3390/ijgi6120398
Received: 24 October 2017 / Revised: 17 November 2017 / Accepted: 1 December 2017 / Published: 4 December 2017
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Abstract
Three-dimensional (3D) road maps have garnered significant attention recently because of applications such as autonomous driving. For 3D road maps to remain accurate and up-to-date, an appropriate updating method is crucial. However, there are currently no updating methods with both satisfactorily high frequency
[...] Read more.
Three-dimensional (3D) road maps have garnered significant attention recently because of applications such as autonomous driving. For 3D road maps to remain accurate and up-to-date, an appropriate updating method is crucial. However, there are currently no updating methods with both satisfactorily high frequency and accuracy. An effective strategy would be to frequently acquire point clouds from regular vehicles, and then take detailed measurements only where necessary. However, there are three challenges when using data from regular vehicles. First, the accuracy and density of the points are comparatively low. Second, the measurement ranges vary for different measurements. Third, tentative changes such as pedestrians must be discriminated from real changes. The method proposed in this paper consists of registration and change detection methods. We first prepare the synthetic data obtained from regular vehicles using mobile mapping system data as a base reference. We then apply our proposed change detection method, in which the occupancy grid method is integrated with Dempster–Shafer theory to deal with occlusions and tentative changes. The results show that the proposed method can detect road environment changes, and it is easy to find changed parts through visualization. The work contributes towards sustainable updates and applications of 3D road maps. Full article
(This article belongs to the Special Issue Mapping for Autonomous Vehicles)
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Open AccessArticle Accuracy Improvement of DGPS for Low-Cost Single-Frequency Receiver Using Modified Flächen Korrektur Parameter Correction
ISPRS Int. J. Geo-Inf. 2017, 6(7), 222; https://doi.org/10.3390/ijgi6070222
Received: 17 June 2017 / Revised: 14 July 2017 / Accepted: 18 July 2017 / Published: 20 July 2017
Cited by 1 | PDF Full-text (10069 KB) | HTML Full-text | XML Full-text
Abstract
A differential global positioning system (DGPS) is one of the most widely used augmentation systems for a low-cost L1 (1575.42 MHz) single-frequency GPS receiver. The positioning accuracy of a low-cost GPS receiver decreases because of the spatial decorrelation between the reference station (RS)
[...] Read more.
A differential global positioning system (DGPS) is one of the most widely used augmentation systems for a low-cost L1 (1575.42 MHz) single-frequency GPS receiver. The positioning accuracy of a low-cost GPS receiver decreases because of the spatial decorrelation between the reference station (RS) of the DGPS and the users. Hence, a network real-time kinematic (RTK) solution is used to reduce the decorrelation error in the current DGPS system. Among the various network RTK methods, the Flächen Korrektur parameter (FKP) is used to complement the current DGPS, because its concept and system configuration are simple and the size of additional data required for the network RTK is small. The FKP was originally developed for the carrier-phase measurements of high-cost GPS receivers; thus, it should be modified to be used in the DGPS of low-cost GPS receivers. We propose an FKP-DGPS algorithm as a new augmentation method for the low-cost GPS receivers by integrating the conventional DGPS correction with the modified FKP correction to mitigate the positioning error due to the spatial decorrelation. A real-time FKP-DGPS software was developed and several real-time tests were conducted. The test results show that the positioning accuracy of the DGPS was improved by a maximum of 40%. Full article
(This article belongs to the Special Issue Mapping for Autonomous Vehicles)
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