Automatic Detection of Topological Changes for Digital Road Map Updating

Boucher, Christophe; Noyer, Jean-Charles

doi:10.1109/tim.2012.2203873

Cited by 11 publications

(4 citation statements)

References 14 publications

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“…Selecting GNSS trajectory points not located on existing roads is usually a necessary stage in road mapping in order to update road networks and refine the geometry of road segments or intersections [5,[15][16][17][18][19]. Then, different algorithms, strategies, and methods for detecting new roads, extensions, and disappearances of existing roads are proposed based on the outlier trajectory points.…”

Section: Road Geometry Data Updatingmentioning

confidence: 99%

A Novel Method of Missing Road Generation in City Blocks Based on Big Mobile Navigation Trajectory Data

Wu³

2019

IJGI

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With the rapid development of cities, the geographic information of urban blocks is also changing rapidly. However, traditional methods of updating road data cannot keep up with this development because they require a high level of professional expertise for operation and are very time-consuming. In this paper, we develop a novel method for extracting missing roadways by reconstructing the topology of the roads from big mobile navigation trajectory data. The three main steps include filtering of original navigation trajectory data, extracting the road centerline from navigation points, and establishing the topology of existing roads. First, data from pedestrians and drivers on existing roads were deleted from the raw data. Second, the centerlines of city block roads were extracted using the RSC (ring-stepping clustering) method proposed herein. Finally, the topologies of missing roads and the connections between missing and existing roads were built. A complex urban block with an area of 5.76 square kilometers was selected as the case study area. The validity of the proposed method was verified using a dataset consisting of five days of mobile navigation trajectory data. The experimental results showed that the average absolute error of the length of the generated centerlines was 1.84 m. Comparative analysis with other existing road extraction methods showed that the F-score performance of the proposed method was much better than previous methods.

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Section: Road Geometry Data Updatingmentioning

confidence: 99%

A Novel Method of Missing Road Generation in City Blocks Based on Big Mobile Navigation Trajectory Data

Wu³

2019

IJGI

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“…Methods inspired from the SLAM (Simultaneous Localization And Mapping) domain can also be employed. The position information given by the navigation map is treated as an observation analogous to observations from other sensors [19]. To be considered as a ground truth, the reference navigation map must be created by an accurate, complete survey.…”

Section: B Navigation System Integrity Monitoringmentioning

confidence: 99%

Sequential FDIA for Autonomous Integrity Monitoring of Navigation Maps on Board Vehicles

Zinoune

Ibañez‐Guzmán

2016

IEEE Trans. Intell. Transport. Syst.

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International audienceThis paper addresses the problem of Fault Detection, Isolation, and Adaptation (FDIA) in navigation systems on board passenger vehicles. The aim is to prevent malfunctions in systems such as advanced driving assistance systems and autonomous driving functions that use data provided by the navigation system. The integrity of the estimation of the vehicle position provided by the navigation system is continuously monitored and assessed. The proposed approach uses an additional estimate of vehicle position that is independent of the navigation system and based on data from standard vehicle sensors. First, fault detection consists in comparing the two estimates using a sequential statistical test to detect discrepancies despite the presence of noise. Second, fault isolation and adaptation is introduced to identify faulty estimates and to provide a correction where necessary. The FDIA framework presented here utilizes repeated trips along the same roads as a source of redundancy. Relevant properties of this formalism are given and verified experimentally using an equipped vehicle in rural and urban conditions and with various map faults. Results show that sequential FDIA performed well, even in difficult GNSS conditions

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“…Similarly the mean distance [23] determines the mean of the calculated distances. Also the Mahalanobis distance [24] can be used as a general metric to compare two tracks.…”

Section: B Error Metricmentioning

confidence: 99%

Towards autonomous self-assessment of digital maps

Hartmann¹,

Gabb

Schweiger

et al. 2014

2014 IEEE Intelligent Vehicles Symposium Proceedings

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Digital maps are becoming increasingly important for driver assistance systems: providing optimal lighting conditions in night scenarios, presenting the road geometry to the driver, or for usage in autonomous driving tasks. However, recorded digital maps own one drawback: due to road changes and inaccurate recordings, discrepancies between the map and the real world exist. Because these discrepancies can lead to severe application level failures, detection of map errors is essential to ensure overall system integrity.This work proposes a new approach to online verification of digital maps for automotive usage. In contrast to previous work, the described system is able to detect errors in front of the vehicle. On the basis of a large database of map geometry and sensor information, a neural network is trained to classify the digital map integrity by optimally fusing different information sources depending on their strength and reliability. Although generally applicable, it is shown that a combination of orthogonal measurement principles is greatly beneficial for this decision task. A radar sensor, infra-red imagery and road geometry information estimated from visible light images are employed as input for the neural fusion. Experiments on realworld data verify the proposed concepts.

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Automatic Detection of Topological Changes for Digital Road Map Updating

Cited by 11 publications

References 14 publications

A Novel Method of Missing Road Generation in City Blocks Based on Big Mobile Navigation Trajectory Data

A Novel Method of Missing Road Generation in City Blocks Based on Big Mobile Navigation Trajectory Data

Sequential FDIA for Autonomous Integrity Monitoring of Navigation Maps on Board Vehicles

Towards autonomous self-assessment of digital maps

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