Meng Zhang

The interaction of automated vehicles with vulnerable road users is one of the greatest challenges in the development of automated driving functions. In order to improve efficiency and ensure the safety of mixed traffic, ADF need to understand the intention of vulnerable road users, to adapt to their driving behavior, and to show its intention. However, this communication may occur in an implicit way, meaning they may communicate with vulnerable road users by using dynamic information, such as speed, distance, etc. Therefore, investigating patterns of implicit communication of human drivers with vulnerable road users is relevant for developing ADF. The aim of this study is to identify the patterns of implicit communication of human drivers with vulnerable road users. For this purpose, the interaction between right-turning motorists and crossing cyclists was investigated at a traffic light controlled urban intersection. In the scenario, motorists and cyclists had a green signal at the same time, but cyclist had right-of-way. Using the Application Platform for Intelligent Mobility Research Intersection, trajectory and video data were recorded at an intersection in Braunschweig, Germany. Data had been recorded for 4 weeks. Based on the criticality metric post-encroachment time and quality of the recorded trajectory, 206 cases of interaction were selected for further analyses. According to the video annotation, when approaching the intersection, three common communication patterns were identified: no yield, motorists, who should yield to cyclists, crossed the intersection first while forcing right-of-way; active yield, motorists, who were in front of cyclists, gave the right-of-way; passive yield, motorists, who were behind cyclists, had to give the right-of-way. The analysis of the trajectory data revealed different patterns of changes in time advantage in these three categories. Additionally, the communication patterns were evaluated with regard to frequency of occurrence, efficiency, and safety. The findings of this study may provide knowledge for the implementation of a communication strategy for ADF, contributing to traffic efficiency as well as ensuring safety in the interaction with vulnerable road users.

In this paper, we investigate the impact of economic policy uncertainty on the conditional dependence between China and U.S. stock markets by employing the Copula-mixed-data sampling framework. In the case of EPU, we consider the global EPU, the American EPU, and the China EPU. The empirical analysis based on the Shanghai Stock Exchange Composite index in China and the S&P 500 index in the U.S. shows that the tail dependence between China and U.S. stock markets is symmetrical, and the t Copula outperforms alternative Copulas in terms of in-sample goodness of fit. In particular, we find that the t Copula-MIDAS model with EPU dominates the traditional time-varying t Copula in terms of in-sample fitting. Moreover, we observe that both the GEPU and AEPU have a significantly positive impact on the conditional dependence between China and U.S. stock markets, whereas CEPU has no significant impact. The tail dependence between China and U.S. stock markets exhibits an increasing trend, particularly in the recent years.

BackgroundBoosting the individual learning passion of medical students is a novel approach to improve their academic performance. It facilitates the medical education reform, motivating both policymakers and educators to focus on the function of positive psychology in the career development of medical students. Therefore, this study aimed to assess the status of two types of learning passion; to clarify the relationship between self-esteem and two types of learning passion among Chinese medical students; to examine the mediating role of psychological capital in the relationship between self-esteem and two types of learning passion, respectively; and to identify the moderating role of professional identity in the relationship between PsyCap and two types of learning passion, respectively.MethodsA cross-sectional online survey was conducted from April to June 2016 in China. A total of 1,218 valid questionnaires were collected from four medical schools.ResultsSelf-esteem significantly and positively influenced medical students’ PsyCap.ConclusionTwo types of learning passion of medical students are positively influenced by self-esteem and PsyCap. Medical students with high-level self-esteem should possess strong PsyCap, which augments their harmonious as well as obsessive learning passion. Moreover, the positive effect of medical students’ PsyCap on harmonious learning passion is more notable among those with a lower professional identity. Finally, this study argues that strengths-based interventions of self-esteem and PsyCap are a beneficial approach for future enhancing learning passion in the domain of medical education.