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With the aid of naturalistic simulations, this paper aims to investigate human behavior during manual and autonomous driving modes in complex scenarios.

The simulation environment is established by integrating virtual reality interface with a micro-simulation model. In the simulation, the vehicle autonomy is developed by a framework that integrates artificial neural networks and genetic algorithms. Human-subject experiments are carried, and participants are asked to virtually sit in the developed autonomous vehicle (AV) that allows for both human driving and autopilot functions within a mixed traffic environment.

Not surprisingly, the inconsistency is identified between two driving modes, in which the AV’s driving maneuver causes the cognitive bias and makes participants feel unsafe. Even though only a shallow portion of the cases that the AV ended up with an accident during the testing stage, participants still frequently intervened during the AV operation. On a similar note, even though the statistical results reflect that the AV drives under perceived high-risk conditions, rarely an actual crash can happen. This suggests that the classic safety surrogate measurement, e.g. time-to-collision, may require adjustment for the mixed traffic flow.

Understanding the behavior of AVs and the behavioral difference between AVs and human drivers are important, where the developed platform is only the first effort to identify the critical scenarios where the AVs might fail to react.

This paper attempts to fill the existing research gap in preparing close-to-reality tools for AV experience and further understanding human behavior during high-level autonomous driving.

This work aims to systematically analyze the inconsistency in driving patterns between manual and autopilot modes in various driving scenarios (i.e. multiple scenes and various traffic conditions) to facilitate user acceptance of AV technology.

A close-to-reality tool for AV experience and AV-related behavioral study. A systematic analysis in relation to the inconsistency in driving patterns between manual and autonomous driving. A foundation for identifying the critical scenarios where the AVs might fail to react.

The purpose of this paper is to present a visual detection approach to predict the poses of target objects placed in arbitrary positions before completing the corresponding tasks in mobile robotic manufacturing systems.

A hybrid visual detection approach that combines monocular vision and laser ranging is proposed based on an eye-in-hand vision system. The laser displacement sensor is adopted to achieve normal alignment for an arbitrary plane and obtain depth information. The monocular camera measures the two-dimensional image information. In addition, a robot hand-eye relationship calibration method is presented in this paper.

First, a hybrid visual detection approach for mobile robotic manufacturing systems is proposed. This detection approach is based on an eye-in-hand vision system consisting of one monocular camera and three laser displacement sensors and it can achieve normal alignment for an arbitrary plane and spatial positioning of the workpiece. Second, based on this vision system, a robot hand-eye relationship calibration method is presented and it was successfully applied to a mobile robotic manufacturing system designed by the authors’ team. As a result, the relationship between the workpiece coordinate system and the end-effector coordinate system could be established accurately.

This approach can quickly and accurately establish the relationship between the coordinate system of the workpiece and that of the end-effector. The normal alignment accuracy of the hand-eye vision system was less than 0.5° and the spatial positioning accuracy could reach 0.5 mm.

This approach can achieve normal alignment for arbitrary planes and spatial positioning of the workpiece and it can quickly establish the pose relationship between the workpiece and end-effector coordinate systems. Moreover, the proposed approach can significantly improve the work efficiency, flexibility and intelligence of mobile robotic manufacturing systems.

Intelligent and connected vehicle technology is in the ascendant. High-level autonomous driving places more stringent requirements on the accuracy and reliability of environmental perception. Existing research works on multitarget tracking based on multisensor fusion mostly focuses on the vehicle perspective, but limited by the principal defects of the vehicle sensor platform, it is difficult to comprehensively and accurately describe the surrounding environment information.

In this paper, a multitarget tracking method based on roadside multisensor fusion is proposed, including a multisensor fusion method based on measurement noise adaptive Kalman filtering, a global nearest neighbor data association method based on adaptive tracking gate, and a Track life cycle management method based on M/N logic rules.

Compared with fixed-size tracking gates, the adaptive tracking gates proposed in this paper can comprehensively improve the data association performance in the multitarget tracking process. Compared with single sensor measurement, the proposed method improves the position estimation accuracy by 13.5% and the velocity estimation accuracy by 22.2%. Compared with the control method, the proposed method improves the position estimation accuracy by 23.8% and the velocity estimation accuracy by 8.9%.

A multisensor fusion method with adaptive Kalman filtering of measurement noise is proposed to realize the adaptive adjustment of measurement noise. A global nearest neighbor data association method based on adaptive tracking gate is proposed to realize the adaptive adjustment of the tracking gate.

The purpose of this paper is to study the pricing efficiency of convertible bonds and arbitrage opportunities between the convertible bonds and the underlying stocks thus improve market efficiency.

Using nonparametric fixed effect panel data model, the authors build pricing model of convertible bonds and obtain fitted value for them. Then the authors constructs simultaneous confidence band for the smooth function to identify mispricing and study the pricing efficiency and arbitrage opportunities of convertible bonds.

Result shows, convertible bonds’ prices largely depend on stock prices. Pricing efficiency does not improve during the past few years as there are quite a few trading opportunities. Arbitrage opportunities increase as the stock prices approach it maxima, and selling opportunities for convertible bonds surpass buying opportunities which indicates that investors use market neutral strategies to arbitrage. Pricing efficiencies varies a lot and it is affected by the features of the stocks and convertible bonds. Index stocks eligible for margin trading with high liquidity enjoy higher pricing efficiency.

The study does not take into account trading cost and risk management measures.

Arbitrage between the underlying and the convertible bonds is profitable and contributes to pricing efficiency therefore should be encouraged. The regulator should pay attention to the extreme mispricing of the underlying and convertible bonds which cannot be corrected by the market as there might be manipulation.

Since traditional pricing methods are based on the framework of non-arbitrage equilibrium with the assumption of balanced and perfect market, there are many restrictions in the pricing process and the practical utility is somewhat limited, and the impractical assumptions lead to model risk. This study uses nonparametric regression to study the pricing of convertible bonds thus circumvents the problem of model risk. Simultaneous confidence band for smooth function identifies mispricing and explicitly reflects the variation of pricing efficiency as well as signalizes trading opportunities. Application of nonparametric regression and simultaneous confidence band in derivative pricing is advantageous in accuracy and simplicity.

This study examines the extent to which educational outcomes are transmitted from mothers to daughters in rural China. An analysis of the 2010 China Family Panel Survey reveals that: (i) how far daughters go in their education is strongly associated with their mothers’ education; (ii) the association between mothers’ and daughters’ educational outcomes in rural China was found to be stronger than the corresponding relationships between mothers and sons, fathers and daughters, and fathers and sons, especially at higher levels of education; and (iii) while having more brothers and being born later worsens daughters’ educational outcomes, mothers’ higher education effectively mitigates these negative effects. These findings add to a growing body of literature and empirical evidence that challenges conventional social mobility research paradigms that neglect mothers’ roles. More importantly, the distinction between mother–daughter relationship and that between fathers and daughters and mothers and sons highlights the fact that education is likely transmitted intergenerationally via mechanisms that differ depending on the gendered parent–child pairs.

The purpose of this paper is to develop parallel algorithms for moving boundary simulations by local remeshing and compose them to a fully parallel simulation cycle for the solution of problems with engineering interests.

The moving boundary problems are solved by unsteady flow computations coupled with six-degrees-of-freedom equations of rigid body motion. Parallel algorithms are developed for both computational fluid dynamics (CFD) solution and grid deformation steps. Meanwhile, a novel approach is developed for the parallelization of the local remeshing step. It inputs a distributed mesh after deformation, then marks low-quality elements to be deleted on the respective processors. After that, a parallel domain decomposition approach is used to repartition the hole mesh and then to redistribute the resulting sub-meshes onto all available processors. Then remesh individual sub-holes in parallel. Finally, the element redistribution is rebalanced.

If the CFD solver is parallelized while the remaining steps are executed in sequential, the performance bottleneck of such a simulation cycle is observed when the simulation of large-scale problem is executed. The developed parallel simulation cycle, in which all of time-consuming steps have been efficiently parallelized, could overcome these bottlenecks, in terms of both memory consumption and computing efficiency.

A fully parallel approach for moving boundary simulations by local remeshing is developed to solve large-scale problems. In the algorithm level, a novel parallel local remeshing algorithm is present. It repartitions distributed hole elements evenly onto all available processors and ensures the generation of a well-shaped inter-hole boundary always. Therefore, the subsequent remeshing step can fix the inter-hole boundary involves no communications.

Social information is crucial to credit ratings and can improve the accuracy of the traditional credit assessment model. Drawing on the resource-based view (RBV) and social capital theory (SCT), this research explores the relationships between corporate social activities, network centrality and corporate credit behavior.

The authors used social network analysis (SNA) and regression analysis to analyze the data collected from 14,544 enterprises on the Alibaba platform.

The results indicate that among the four types of social activities, the number of corporate questions and posts shows a positive relationship with credit behavior; while the number of corporate comments has negative relationship with credit behavior. Further, degree and betweenness centralities mediate the relationship between the number of corporate questions, posts and comments with credit behavior.

This study contributes to the literature on non-financial factors (soft information) by exploring the social behavioral factors related to corporate credit. In addition, this study offers a new theoretical lens and reasonable explanations for investigating the relationship between corporate social activities, network centrality and credit behavior from the perspective of the resource-based view, while most studies are predictive and methodological. Moreover, this study provides new insights for platforms to evaluate enterprise credit and for managers to improve credit behavior.

Blockchain is a distributed ledger technology that uses cryptography to ensure transmission and access security, which provides solutions to numerous challenges to complex supply networks. The purpose of this paper is to empirically test the impact of blockchain implementation on shareholder value varying from internal and external complexity from the complex adaptive systems (CASs) perspective. It further explores how business diversification, supply chain (SC) concentration and environmental complexity affect the relationship between blockchain implementation and shareholder value.

Based on 138 blockchain implementation announcements of listed companies on the Chinese A-share stock market, the authors use event study methodology to evaluate the impact of blockchain implementation on shareholder value.

The results show that blockchain implementation has a positive impact on shareholder value, and this impact will be moderated by business diversification, SC concentration and environmental complexity. In addition, environmental complexity exerts a moderating effect on SC concentration. In the post hoc analysis, the authors further explore the impact of blockchain implementation on long-term operational performance.

This is the first research empirically examining the effect of blockchain implementation on shareholder value varying from internal and external complexity from the CASs perspective. This paper provides evidence of the different effects of blockchain implementation on short- and long-term performance. It adds to the interdisciplinary research of information systems (IS) and operations management (OM).