Search results

The growing availability of naturalistic driving datasets (NDDs) presents a valuable opportunity to develop various models for autonomous driving. However, while current NDDs include data on vehicles with and without intended driving behavior changes, they do not explicitly demonstrate a type of data on vehicles that intend to change their driving behavior but do not execute the behaviors because of safety, efficiency, or other factors. This missing data is essential for autonomous driving decisions. This study aims to extract the driving data with implicit intentions to support the development of decision-making models.

According to Bayesian inference, drivers who have the same intended changes likely share similar influencing factors and states. Building on this principle, this study proposes an approach to extract data on vehicles that intended to execute specific behaviors but failed to do so. This is achieved by computing driving similarities between the candidate vehicles and benchmark vehicles with incorporation of the standard similarity metrics, which takes into account information on the surrounding vehicles' location topology and individual vehicle motion states. By doing so, the method enables a more comprehensive analysis of driving behavior and intention.

The proposed method is verified on the Next Generation SIMulation dataset (NGSim), which confirms its ability to reveal similarities between vehicles executing similar behaviors during the decision-making process in nature. The approach is also validated using simulated data, achieving an accuracy of 96.3 per cent in recognizing vehicles with specific driving behavior intentions that are not executed.

This study provides an innovative approach to extract driving data with implicit intentions and offers strong support to develop data-driven decision-making models for autonomous driving. With the support of this approach, the development of autonomous vehicles can capture more real driving experience from human drivers moving towards a safer and more efficient future.

The purpose of this paper is to lay a theoretical foundation for identifying operational information units for library and information professional activities in the context of scholarly communication.

The study adopts a deduction-verification approach to formulate a typology of units for scholarly information. It first deduces possible units from an existing conceptualization of information, which defines information as the combined product of data and meaning, and then tests the usefulness of these units via two empirical investigations, one with a group of scholarly papers and the other with a sample of scholarly information users.

The results show that, on defining an information unit as a piece of information that is complete in both data and meaning, to such an extent that it remains meaningful to its target audience when retrieved and displayed independently in a database, it is then possible to formulate a hierarchical typology of units for scholarly information. The typology proposed in this study consists of three levels, which in turn, consists of 1, 5 and 44 units, respectively.

The result of this study has theoretical implications on both the philosophical and conceptual levels: on the philosophical level, it hinges on, and reinforces the objective view of information; on the conceptual level, it challenges the conceptualization of work by IFLA’s Functional Requirements for Bibliographic Records and Library Reference Model but endorses that by Library of Congress’s BIBFRAME 2.0 model.

It calls for reconsideration of existing operational units in a variety of library and information activities.

The study strengthens the conceptual foundation of operational information units and brings to light the primacy of “one work” as an information unit and the possibility for it to be supplemented by smaller units.

This paper aims to identify and determine regional tourism axes in Hormozgan Province, Iran, as a region with significant potential

The research method is quantitative and uses the fuzzy accreditation tool and TOPSIS model; the identification, determination and ranking of regional tourism axes have been performed by analyzing the spatial distribution of tourism attractions in the GIS environment.

The results show that given the capacities of Hormozgan Province, at least 15 axes are recognizable. This paper highlights regional tourism planning as a tool for urban and rural socio-economic development in potential provinces such Hormozgan.

This study provides a number of practical implications for regional tourism development as follows: it identifies some of the most important potential axes in Hormozgan Province, which can be considered as investment areas in the national and regional tourism development strategy. The spatial results of this study could be embedded in all urban and rural developmental plans in the province. Tourism investment should shift its spatial concentration from the spot approach, especially islands and cities, to the axis approach while equipping those axes as comprehensive spatial strategic regional tourism plans. Sectoral tourism in each sector including sports, economy and nature could be planned as if sectoral institutions and organizations are going to develop their own tourism goals.