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It would take billions of miles’ field road testing to demonstrate that the safety of automated vehicle is statistically significantly higher than the safety of human driving because that the accident of vehicle is rare event.

This paper proposes an accelerated testing method for automated vehicles safety evaluation based on improved importance sampling (IS) techniques. Taking the typical cut-in scenario as example, the proposed method extracts the critical variables of the scenario. Then, the distributions of critical variables are statistically fitted. The genetic algorithm is used to calculate the optimal IS parameters by solving an optimization problem. Considering the error of distribution fitting, the result is modified so that it can accurately reveal the safety benefits of automated vehicles in the real world.

Based on the naturalistic driving data in Shanghai, the proposed method is validated by simulation. The result shows that compared with the existing methods, the proposed method improves the test efficiency by 35 per cent, and the accuracy of accelerated test result is increased by 23 per cent.

This paper has three contributions. First, the genetic algorithm is used to calculate IS parameters, which improves the efficiency of test. Second, the result of test is modified by the error correction parameter, which improves the accuracy of test result. Third, typical high-risk cut-in scenarios in China are analyzed, and the proposed method is validated by simulation.

The purpose of this study is to investigate the mechanism of shared leadership on team members’ innovative behavior.

Paired questionnaires were collected from 89 scientific research teams in the Beijing-Tianjin-Hebei region of China at two-time points with a time lag of 4 months. Then multilevel structural equation model method was applied to analyze the multiple mediating effects.

This study finds that: the form of shared leadership in scientific research teams of universities; shared leadership has a positive impact on team members’ innovative behavior; there are multiple mediations in the relationship including synchronization and sequence of creative self-efficacy and achievement motivation.

According to the “stimulus-organism-response” model, this paper has constructed a multi-level theoretical model that shared leadership influences individual innovation behavior and reveals the “black box” from the perspective of psychological mechanism. It not only verifies that “can-do” shapes “willing to do” but also makes up for the gap of an empirical test of the effectiveness of shared leadership in scientific research teams of universities. Besides, the formal scale of shared leadership in the Chinese situation is revised, which can provide a reference for future empirical research on shared leadership. The research conclusions provide new ideas for improving the management of scientific research teams in universities.