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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.

This study examines the effect of supply chain (SC) learning (i.e. supplier and customer learnings) on green innovation (i.e. green product and process innovations) and investigates the moderating role of green transformational leadership in the SC learning-green innovation linkage in the construction industry.

Data are gathered from construction firms in Vietnam by a questionnaire survey. Hypotheses of the study framework are tested by hierarchical regression analysis.

Both supplier and customer learnings have positive effects on green innovation (both green process and product innovations). Furthermore, green transformational leadership moderates the linkage between supplier learning and green innovation but does not moderate the linkage between customer learning and green innovation.

Construction firms need to constantly develop capabilities of SC learning for promoting their green innovation.

The present study is one of the first attempts in construction that investigates the importance of SC learning to achieving green innovation as well as the role of green transformational leadership for strengthening the effect of green learning on green innovation.

It has been ten years since the policy was implemented, but the effect of the policy needs to be tested empirically. This paper aims to explore the mechanism of policy influence on regional innovation ability by measuring the effectiveness of policy by innovation ability indicators. Further, it reflects the problems in the process of the transformation and development of resource-based cities in recent years and points out the direction for the development of the cities in the future. In addition, this paper discusses the differences between regions and cities in China and seeks the path to narrow the gap.

This paper mainly uses the difference-in-difference method for the research. This study divided China’s resource-based cities and non-resource-based cities into experimental groups and control groups, and explored the effect of the transformation and development of resource-based cities and the changes of their innovation ability under the influence of the National Sustainable Development Plan for Resource-based Cities (NSDPRC). More carefully, this paper uses the fixed effects regression model, propensity score matching method, bootstrap method and other methods to improve the empirical results.

This paper finds that NSDPRC significantly improves the innovation ability of resource-based cities, although there is some lag in this effect. Research on the influence mechanism of policies shows that NSDPRC improves the marketization degree of resource-based cities and reduces the proportion of the secondary industry in such cities. Finally, the results of the heterogeneity analysis confirm that policies are more popular in western China and that resource-based cities in growth, maturity and decline are more vulnerable to policy influence. The development of policy effectiveness also requires the size of a city, and maintaining a healthy and reasonable scale is necessary for urban development.

First, the existing research on the development of resource-based cities is mainly from the perspective of economy and environment, but rarely from the perspective of innovation ability, and the index to measure urban development is relatively single. This paper will compensate for this deficiency. Second, different from the European and American countries that have basically completed the industrial transformation, the research on Chinese cities will provide a reference for the transformation of developing countries. Finally, from the perspective of resource endowment theory and innovation theory, this paper discusses the influence of SDPNRBC mechanism on the innovation ability improvement of resource-based cities, and further improves and enriches the theory.