Most advanced autonomous driving systems (ADS) today rely on the prior creation of high-definition maps (HD maps). This process is expensive and needs to be performed frequently to keep up with the ...changing conditions of the road environment. Creating accurate navigation maps online is an alternative to reduce the cost and broaden the current operational design domains (ODD) of modern ADS. This paper offers a snapshot of the state of the art in drivable area estimation, which is an essential technology to deploy ADS in ODDs where HD maps are limited or unavailable. The proposed review introduces a novel architecture breakdown that fits learning-based and non-learning-based techniques and allows the analysis of a set of impactful and recent drivable area algorithms. In addition to that, complimentary information for practitioners is provided: (i) an assessment of the influence of modern sensing technologies on the task under study and (ii) a selection of relevant datasets for evaluation and benchmarking purposes.
Systematic mapping assesses the nature of an evidence base, answering how much evidence exists on a particular topic. Perhaps the most useful outputs of a systematic map are an interactive database ...of studies and their meta-data, along with visualisations of this database. Despite the rapid increase in systematic mapping as an evidence synthesis method, there is currently a lack of Open Source software for producing interactive visualisations of systematic map databases. In April 2018, as attendees at and coordinators of the first ever Evidence Synthesis Hackathon in Stockholm, we decided to address this issue by developing an R-based tool called EviAtlas, an Open Access (i.e. free to use) and Open Source (i.e. software code is freely accessible and reproducible) tool for producing interactive, attractive tables and figures that summarise the evidence base. Here, we present our tool which includes the ability to generate vital visualisations for systematic maps and reviews as follows: a complete data table; a spatially explicit geographical information system (Evidence Atlas); Heat Maps that cross-tabulate two or more variables and display the number of studies belonging to multiple categories; and standard descriptive plots showing the nature of the evidence base, for example the number of studies published per year or number of studies per country. We believe that EviAtlas will provide a stimulus for the development of other exciting tools to facilitate evidence synthesis.
•A comprehensive survey of high definition map for the intelligent connected vehicle.•A forward look from the history, standardization, building, and maintenance to its application.•Comparison ...between standards, models, algorithms, and open-source datasets.•A list of the overall challenges for the development of high definition map
An accurate and up-to-date High Definition (HD) Map is critical for an intelligent vehicle to drive safely and effectively. Although research in this area is growing, there is still a lack of clarity in defining HD maps for intelligent connected vehicles (ICVs). This gap in knowledge is particularly challenging for new researchers, who often struggle to find suitable HD map datasets due to a lack of comprehensive reviews on current HD map products, as far as the authors’ knowledge. Thus, this article aims to bridge this gap by providing a thorough analysis of the core ideas of HD map technology. Initially, this paper presents the brief history of HD map. Following this, it describes the taxonomy and ontology of HD maps, complete with the HD map contents and existing standards. An insight into the mapping process is also given by discussing the algorithms used for creating and updating HD maps. This manuscript also lists current HD map products and the open-sourced dataset available for interested researchers in this space. As part of this study, the authors also describe common applications of HAD maps in ICVs. Finally, the article highlight the key research challenges and potential future directions in this field. Addressing these challenges is vital for the advancement and integration of HD maps for ICVs.
Accurate digital maps play a crucial role in various location-based services and applications. However, store information is usually missing or outdated in current maps. In this paper, we propose ...CrowdGIS, an automatic store self-updating system for digital maps that leverages street views and sensing data crowdsourced from mobile users. We first develop a new weighted artificial neural network to learn the underlying relationship between estimated positions and real positions to localize user's shooting positions. Then, a novel text detection method is designed by considering two valuable features, including the color and texture information of letters. In this way, we can recognize complete store name instead of individual letters as in the previous study. Furthermore, we transfer the shooting position to the location of recognized stores in the map. Finally, CrowdGIS considers three updating categories (replacing, adding, and deleting) to update changed stores in the map based on the kernel density estimate model. We implement CrowdGIS and conduct extensive experiments in a real outdoor region for 1 month. The evaluation results demonstrate that CrowdGIS effectively accommodates store variations and updates stores to maintain an up-to-date map with high accuracy. Note to Practitioners -This paper was motivated by the problem of automatically updating digital maps in a manner of mobile crowdsensing. Existing approaches can update stores in maps through a manual survey or update roads automatically from mobile crowdsensing data. Since the store information is a crucial component in digital map, this paper suggests a novel approach to automatically update stores in digital maps through mobile crowdsensing. This is necessary, in general, because the accuracy of digital map will directly affect the quality of various location-based services. Therefore, the system proposed in this paper is useful for engineers and developers to obtain precise digital maps for localization, navigation, automatic drive, etc.
Massively Multiplayer Online Games (MMOGs) provide many opportunities for scientists. Previous research ranges from personality trait prediction to alternative cancer treatments. However, there is an ...ongoing debate on whether these virtual worlds are able to represent real world scenarios. The mapping of online and offline findings is key to answering this question. Our work contributes to this discussion by providing an overview of the findings from network-based team and leadership research and by matching them with concrete results from our MMOG case study. One major finding is that team size matters. We show that high diversity in the type of teams is a major challenge, especially when combined with the immense amount of data in MMOGs. In our work, we discuss these issues and show that a well-grounded understanding of the data and the game environment makes it possible to overcome these limitations. Besides the team size, the aggregation periods play an important role. Regarding MMOGs as research environments, we show that it is important to pay close attention to the specific game-related contexts, the incentive structures, and the downside risks. Methodologically, we apply support and communication networks to show the influence of certain group-based measures (e.g., density, transitivity) as well as leadership-centered characteristics (e.g., k-core, group centrality, betweenness centralization) on team performance. Apart from our findings on centralization in communication networks, we are able to demonstrate that our results confirm the theoretical predictions which suggest that the behavioral patterns observed in MMOG teams are comparable to those observed in offline work teams.
This paper describes the development of a robot that can move through a forest environment and automatically generates three-dimensional (3D) digital maps. We designed and constructed the robot, ...equipping it with a rocker-bogie mechanism composed of six drive wheels and four steering motors. This mechanics enables the robot to move damaging a forest as little as possible while negotiating the obstacle in it. This robot is equipped with a 3D light detection and ranging sensor, and it uses the normal distributions transform algorithm to construct 3D digital maps. We tested the generation of a 3D digital map of 50 m
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of a sloped forest environment and verified the generated map by comparing it with a map constructed manually by engineers. The comparison revealed an error of less than 0.15 m.
Understanding users' visual attention on websites is paramount to enhance the browsing experience, such as providing emergent information or dynamically adapting Web interfaces. Existing approaches ...to accomplish these challenges are generally based on the computation of salience maps of static Web interfaces, while websites increasingly become more dynamic and interactive. This paper proposes a method and provides a proof-of-concept to predict user's visual attention on specific regions of a website with dynamic components. This method predicts the regions of a user's visual attention without requiring a constant recording of the current layout of the website, but rather by knowing the structure it presented in a past period. To address this challenge, the concept of visit intention is introduced in this paper, defined as the probability that a user, while browsing, will fixate their gaze on a specific region of the website in the next period. Our approach uses the gaze patterns of a population that browsed a specific website, captured via an eye-tracker device, to aid personalized prediction models built with individual visual kinetics features. We show experimentally that it is possible to conduct such a prediction through multilabel classification models using a small number of users, obtaining an average area under curve of 84.3%, and an average accuracy of 79%. Furthermore, the user's visual kinetics features are consistently selected in every set of a cross-validation evaluation.
Lane-level digital maps are crucial to advanced driver assistance systems (ADAS) and autonomous driving, since they can simplify driving tasks and enhance system performance by providing strong ...priors about the driving environment. However, the high cost of current map generation systems prevents their benefits to normal commercial cars as they usually depend on specialized sensors and need great manual postprocessing. In this paper, a low-cost solution is proposed for automatic generation of a precise lane-level map by using conventional sensors that have been already installed in contemporary cars. It mainly consists of two modules, i.e., road orthographic image generation and lane graph construction. First, the global map is divided into fixed local segments based on the road network topology. With the reference of the local map segments, the bird's eye view images of the road surface are accumulated by fusing GPS, INS, and visual odometry and subsequently integrated into synthetic orthographic images. Next, the driving lane information is extracted from the road orthographic images and a large amount of vehicle trajectories. Such information is then used to construct a lane graph of the map based on the sophisticated lane models we proposed without manual processing. Experiments show promising results of the automatic map generation of the real-world roads, which substantiated the effectiveness of the proposed approach. Such a system can offer increased value and promote the automation level for today's commercial cars without being supplemented additional sensors.
This paper proposes a method for achieving improved ego-vehicle global localization with respect to an approaching intersection, which is based on the alignment of visual landmarks perceived by the ...on-board visual system, with the information from a proposed extended digital map (EDM). The visual system relies on a stereovision system that provides a detailed 3-D description of the environment, including road landmark information (lateral lane delimiters, painted traffic signs, curbs, and stop lines) and dynamic environment information (other vehicles). An EDM is proposed, which enriches the standard map information with a detailed description of the intersection required for current lane identification, landmark alignment, and ego-vehicle accurate global localization. A novel approach for lane-delimiter classification, which is necessary for the lane identification, is also presented. An original solution for identifying the current lane, combining visual and map information with the help of a Bayesian network (BN), is proposed. Extensive experiments have been performed, and the results are evaluated with a Global Navigation Satellite System of high accuracy (2 cm). The achieved global localization accuracy is of submeter level, depending on the performance of the stereovision system.
The International GNSS Service (IGS) diurnal ROTI maps ionospheric product was developed to characterize ionospheric irregularities occurrence over the Northern hemisphere and has been available for ...the community since 2014. Currently, the diurnal ROTI maps database hosted by NASA CDDIS covers the period from 2010 to now. Here, we report the ROTI maps product operational status and important changes in the product availability and access. Apart from actual ROTI maps product production, we work on the extension of ROTI maps to cover not only the Northern hemisphere but also the area of the Southern hemisphere and equatorial/low latitude region. Such extended ROTI maps are important for ionospheric irregularities climatology research and ionospheric responses to space weather. We present recent development toward the new ROTI maps product and the updated data format. To evaluate extended the ROTI maps performance, we analyzed the ability to represent key features of ionospheric irregularity occurrence over the Southern hemisphere and low latitudes. For auroral and midlatitudes, we present the cross-comparison of ROTI-derived irregularities patterns over the Northern and Southern hemispheres. For low latitudes, we examined the sensitivity of the resulted ROTI maps to detect plasma irregularities associated with equatorial plasma bubbles development for low, middle, and high solar activity periods.
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