Map++: A Crowd-sensing System for Automatic Map Semantics Identification

Digital maps have become a part of our daily life with a number of commercial and free map services. These services have still a huge potential for enhancement with rich semantic information to support a large class of mapping applications. In this paper, we present Map++, a system that leverages standard cell-phone sensors in a crowdsensing approach to automatically enrich digital maps with different road semantics like tunnels, bumps, bridges, footbridges, crosswalks, road capacity, among others. Our analysis shows that cell-phones sensors with humans in vehicles or walking get affected by the different road features, which can be mined to extend the features of both free and commercial mapping services. We present the design and implementation of Map++ and evaluate it in a large city. Our evaluation shows that we can detect the different semantics accurately with at most 3% false positive rate and 6% false negative rate for both vehicle and pedestrian-based features. Moreover, we show that Map++ has a small energy footprint on the cell-phones, highlighting its promise as a ubiquitous digital maps enriching service.Comment: Published in the Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking (IEEE SECON 2014

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Advanced driver assistance systems for heavy duty vehicles, such as lookahead cruise and gearshift controllers, rely on high quality map data. Current digital maps do not offer the required level of road grade information. This contribution presents an algorithm for on-board road grade estimation based on fusion of GPS and vehicle sensor data with measurements from previous runs over the same road segment. An incremental update scheme is utilized to ensure that data storage requirements are independent of the number of measurement runs. Results of the implemented system based on six traversals of a known road with three different vehicles are presented.QC 20120216</p

High-Level Interpretation of Urban Road Maps Fusing Deep Learning-Based Pixelwise Scene Segmentation and Digital Navigation Maps

This paper addresses the problem of high-level road modeling for urban environments. Current approaches are based on geometric models that fit well to the road shape for narrow roads. However, urban environments are more complex and those models are not suitable for inner city intersections or other urban situations. The approach presented in this paper generates a model based on the information provided by a digital navigation map and a vision-based sensing module. On the one hand, the digital map includes data about the road type (residential, highway, intersection, etc.), road shape, number of lanes, and other context information such as vegetation areas, parking slots, and railways. On the other hand, the sensing module provides a pixelwise segmentation of the road using a ResNet-101 CNN with random data augmentation, as well as other hand-crafted features such as curbs, road markings, and vegetation. The high-level interpretation module is designed to learn the best set of parameters of a function that maps all the available features to the actual parametric model of the urban road, using a weighted F-score as a cost function to be optimized. We show that the presented approach eases the maintenance of digital maps using crowd-sourcing, due to the small number of data to send, and adds important context information to traditional road detection systems

Road Feature Extraction from High Resolution Aerial Images Upon Rural Regions Based on Multi-Resolution Image Analysis and Gabor Filters

Accurate, detailed and up-to-date road information is of special importance in geo-spatial databases as it is used in a variety of applications such as vehicle navigation, traffic management and advanced driver assistance systems (ADAS). The commercial road maps utilized for road navigation or the geographical information system (GIS) today are based on linear road centrelines represented in vector format with poly-lines (i.e., series of nodes and shape points, connected by segments), which present a serious lack of accuracy, contents, and completeness for their applicability at the sub-road level. For instance, the accuracy level of the present standard maps is around 5 to 20 meters. The roads/streets in the digital maps are represented as line segments rendered using different colours and widths. However, the widths of line segments do not necessarily represent the actual road widths accurately. Another problem with the existing road maps is that few precise sub-road details, such as lane markings and stop lines, are included, whereas such sub-road information is crucial for applications such as lane departure warning or lane-based vehicle navigation. Furthermore, the vast majority of roadmaps aremodelled in 2D space, whichmeans that some complex road scenes, such as overpasses and multi-level road systems, cannot be effectively represented. In addition, the lack of elevation information makes it infeasible to carry out applications such as driving simulation and 3D vehicle navigation

Uncoupled GPS Road Constrained Positioning based on Constrained Kalman Filtering

Car navigation systems take advantage of the synergies between the Global Positioning System (GPS) and digital road maps. For this kind of applications the digital road maps can provide a priori information to improve the positioning accuracy. This paper presents a method for the estimation of the user’s position, based on GPS positioning estimates, constrained to a road map. A low-cost GPS receiver was assumed as being the source of the positioning information. The techniques used in the proposed estimator were developed taking in consideration that the platforms where typically they would be implemented are characterized by having reduced computational capabilities. The algorithm’s positioning accuracy was characterized based on real data from a low-cost GPS receiver installed in a car. Different scenarios were used in the field trials in order to evaluate the impact of the satellite constellation visibility and geometry in the algorithm’s performance

Review on Active and Passive Remote Sensing Techniques for Road Extraction

Digital maps of road networks are a vital part of digital cities and intelligent transportation. In this paper, we provide a comprehensive review on road extraction based on various remote sensing data sources, including high-resolution images, hyperspectral images, synthetic aperture radar images, and light detection and ranging. This review is divided into three parts. Part 1 provides an overview of the existing data acquisition techniques for road extraction, including data acquisition methods, typical sensors, application status, and prospects. Part 2 underlines the main road extraction methods based on four data sources. In this section, road extraction methods based on different data sources are described and analysed in detail. Part 3 presents the combined application of multisource data for road extraction. Evidently, different data acquisition techniques have unique advantages, and the combination of multiple sources can improve the accuracy of road extraction. The main aim of this review is to provide a comprehensive reference for research on existing road extraction technologies.Peer reviewe

Generation of road accident risk maps

http://citta-conference.fe.up.pt/editions/2013Knowing the factors that affect the likelihood of an accident occurrence has been increasingly challenging to the researchers given the huge social and financial costs that derive from road accidents. In Portugal, developments in this area have mainly involved interurban roads studies. However, according to ANSRi, about 70% of Portuguese road accidents occur in urban spaces, a trend common to most European countries. The lack of national or local information systems containing geo-referenced road accidents, geometric characteristics of roads, among others, hamper the creation of tools that help to assess the risk of exposure at a micro level, i.e. road intersections. The weaknesses mentioned above led us towards the implementation of models in a GIS-based environment in order to estimate the frequency of accidents for urban areas according to several breakdowns: road element, type of accident and the inclusion of explanatory variables related to road environment. One of the challenges faced by researchers when applying these models is the absence of data or its poor quality. Therefore, it is necessary to cross and analyse information from different sources, such as traffic variables (from model transportation planning), digital cartographic data, and other geometric variables, that may not be obtained in a direct way (e.g. using OpenStreetMap or Google Maps). In a further step, the estimation models will be programmed and applied according to the type of road element (e.g. intersections, roundabouts, segments). Finally, new information will be generated with all inputted data: a digital map with the number of accidents per road element. Such figures will need to be converted into something more meaningful for potential users, such as levels/categories (e.g. high, medium and low risk of exposure), which can be mapped. This paper proposes a methodology for automatic generation of road accident maps with those levels/categories. Thus, maps will serve as a decision support tool not only to insurers (who are likely to tax drivers more effectively, according to their exposure to risk), but also to drivers themselves (through generation of alarms that will allow them to tailor their driving performance), envisaging road safety improvement

Use of satellite images for broad-scale modelling of conservation areas for wolves in the Carpathian Mountains, central Europe

This study analysed the spatial structure of the Carpathian Mountains, in Central Europe, considering it a unit that extends across national boundaries, and assessing the suitability of areas were wolves could be conserved. Physical characteristics of the area were extracted from NOAA-AVHRR NDVI. A set of 9 images from different periods of the year was used to parameterise the phenological variability of the area. Digital maps of road networks, human settlements and a DEM were integrated in a GIS. Locations of wolf presence were used to extract “optimal” environmental characteristics that served as reference for estimating the degree of suitability over the whole area. Results show that most of the Carpathian Mountains are highly suitable for the wolf and that highly suitable areas are actually inhabited by the present population of wolf. These are also the area most phenologically stable