1,721,013 research outputs found
Socially Acceptable Route Planning and Trajectory Behavior Analysis of Personal Mobility Device for Mobility Management with Improved Sensing
No full textIn urban cities, with increasing acceptability of shared spaces used by pedestrians and personal mobility devices (PMDs), there is need for pragmatic socially ac-ceptable path planning and navigation management policies. Hence, we propose a socially acceptable global route planner and assess the legibility of the resulting global route. Our approach proposed for choosing global route avoids streets penetrating shared spaces and main routes with high probability of dense usage. The experimental study shows that socially acceptable routes can be effectively found with an average of 10 % increment of route length with optimal hyperpa-rameters. This helps PMDs to reach the goal while taking a socially acceptable and safe route with minimal interaction of different PMDs and pedestrians. When PMDs interact with pedestrians and other types of PMDs in shared spaces, mi-cro-mobility simulations are of prime usage for acceptable and safe navigation policy. Social force models being state of the art for pedestrian simulation are cal-ibrated for capturing random movements of pedestrian behavior. Social force model with calibration can imitate the required behavior of PMDs in a pedestrian mix navigation scheme. Based on calibrated models, simulations on shared space links and gate structures are performed to assist policies related to deciding wait-ing and stopping time. Also, based on simulated PMDs interaction with pedestri-ans, location data with finer resolution can be obtained if the resolution of GPS sensor is 0.2 m or less. This will help in formalizing better modelling and hence better micro-mobility policies
Integrating Multi-sourced Sensor Data for Enhanced Traffic State Estimation
No full textAccurate traffic state estimation, vital for managing urban congestion, is often achieved through simulation. Real-time data are invaluable for this, yet obtaining multisensor data is challenging and costly. To bridge this gap, leveraging crowdsourced data from third-party sources, despite its anonymity, enriches available information for precise estimation. This passive crowdsourced data is often reported as estimated time of arrival (ETA) and color-coded traffic patterns. The article introduces an effective calibration approach for a mesoscopic traffic simulation of a complex urban arterial network, primarily relying on crowdsourced data and incorporating sensor data, if accessible. The approach employs a nested genetic algorithm (NGA) with average speed data, calculated using ETA to estimate vehicle counts, eliminating the need for time-consuming field surveys. A custom mutation operator-speed deviation adaptive gene mutator, is introduced for generating vehicle counts to replicate real-world traffic conditions in the intermediate time steps of simulation. Additionally, a route selection algorithm (RSA) is developed using color-coded tracks from Google Maps for prioritizing routes based on congestion patterns. The study demonstrates a novel data-fusion technique, combining sensors with passive crowdsourced information for accurate traffic speed estimation. The proposed methodology was applied to two case studies of urban arterial networks. The values obtained from simulations during validation showed promising proximity to real values, yielding a mean absolute percentage error (MAPE) of 0.29% for a simpler network and 6.31% for the best ten routes, and 10.33% for all of the 15 priority routes within a complex network.
RelMobNet: End-to-end relative camera pose estimation using a robust two-stage training
No full textRelative camera pose estimation, i.e. estimating the translation and rotation vectors using a pair of images taken in different locations, is an important part of systems in augmented reality and robotics. In this paper, we present an end-to-end relative camera pose estimation network using a siamese architecture that is independent of camera parameters. The network is trained using the Cambridge Landmarks data with four individual scene datasets and a dataset combining the four scenes. To improve generalization, we propose a novel two-stage training that alleviates the need of a hyperparameter to balance the translation and rotation loss scale. The proposed method is compared with one-stage training CNN-based methods such as RPNet and RCPNet and demonstrate that the proposed model improves translation vector estimation by 16.11%, 28.88%, and 52.27% on the Kings College, Old Hospital, and St Marys Church scenes, respectively. For proving texture invariance, we investigate the generalization of the proposed method augmenting the datasets to different scene styles, as ablation studies, using generative adversarial networks. Also, we present a qualitative assessment of epipolar lines of our network predictions and ground truth poses
Infra Sim-to-Real: An efficient baseline and dataset for Infrastructure based Online Object Detection and Tracking using Domain Adaptation
No full textIncreasing usage of traffic cameras provides an opportunity to utilize them for smart city applications. However, the efficacy of such systems is determined by their ability to detect and track objects of interest from diverse viewpoints accurately. This is challenging due to the diverse viewpoints, elevations, and distinct properties of camera sensors. Thus, to ensure robust performance, the training dataset should cover many variations, including viewpoints, illumination changes, and diverse weather conditions. However, constructing such a dataset is expensive in terms of data collection and annotation. This paper proposes an unsupervised domain adaptation approach wherein a synthetic dataset is generated using a simulator and subsequently used to ensure performance consistency of multi-object-tracking (MOT) algorithms across a diverse range of manually annotated natural scenes. Towards this end, we emphasize achieving domain invariant object detection by combining image stylization and class-balancing augmentation. Furthermore, we extend the robust detection algorithm to track detected objects across a large time scale using feature embeddings generated by the detector. Based on qualitative and quantitative results, we demonstrate the viability of such a system that is invariant to illumination, weather, viewpoint, and scene changes while providing a baseline for future research. Codebase and datasets would be made available at https://github.com/pranjay-dev/IS2R
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
스마트시티 모빌리티 서비스를 위한 멀티모달 데이터 활용
No full text학위논문(석사) - 한국과학기술원 : 로봇공학학제전공, 2023.2,[iv, 65 p. :]With the rapid increase of population in cities along with connected technologies, cities are made smart to facilitate the populace at their best. Mobility and transport facilitation is the key to a healthy and commutable smart city. With this vision Intelligent Transportation Systems (ITS) have historically been introduced to increase the transportation network performancesallowing for the optimization of several indicators that are strictly related such as travel time, emissions, usability, and safety. The effectiveness of all ITS proposed strategies is mainly based on the ideas of traffic parameter predictions and controlling or anticipating driver’s / traveler’s behavior. Indeed, all relevant policies such as driving guidance, mobility information systems design, and traffic management are based on the consistency between the decision/control variables and the actual traffic parameters (congestion, travel times, vehicle tracking, etc). So, in this dissertation, the goal is to generate intelligence out of structured data for facilitating respective smart mobility services. In the first use case, we leveraged crowdsourced data by road users to get congestion intelligence. Using that, a simple control algorithm for adaptive signaling in developing countries was proposed. Further, we explored socially acceptable route planning for personal mobility devices and safe route planning for pedestrian micro-mobility during the pandemic. For the smart surveillance services use case, we collaborated on robust and domain-invariant online object detection and tracking. In yet another mobility service, aimed at driver assistance using accident event data and corresponding street view images, context-specific accident-prone features are identified. The detected accident-prone features are to be notified to drivers in a proposed head-up display to enhance their decision-making. The ultimate goal is to power new fine-processed data that can be used and reused across applications and businesses, with each data modality adding valuable intelligence one over the other towards autonomous and smart city mobility.한국과학기술원 :로봇공학학제전공
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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