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With the continuous technological development of automated driving and expansion of its application scope, the types of on-board equipment continue to be enriched and the computing capabilities of on-board equipment continue to increase and corresponding applications become more diverse. As the applications need to run on on-board equipment, the requirements for the computing capabilities of on-board equipment become higher. Mobile edge computing is one of the effective methods to solve practical application problems in automated driving.

In this study, in accordance with practical requirements, this paper proposed an optimal resource management allocation method of autonomous-vehicle-infrastructure cooperation in a mobile edge computing environment and conducted an experiment in practical application.

The design of the road-side unit module and its corresponding real-time operating system task coordination in edge computing are proposed in the study, as well as the method for edge computing load integration and heterogeneous computing. Then, the real-time scheduling of highly concurrent computation tasks, adaptive computation task migration method and edge server collaborative resource allocation method is proposed. Test results indicate that the method proposed in this study can greatly reduce the task computing delay, and the power consumption generally increases with the increase of task size and task complexity.

The results showed that the proposed method can achieve lower power consumption and lower computational overhead while ensuring the quality of service for users, indicating a great application prospect of the method.

Autonomous driving depends on the collection, processing and analysis of environmental information and vehicle information. Environmental perception and processing are important prerequisite for the safety of self-driving of vehicles; it involves road boundary detection, vehicle detection, pedestrian detection using sensors such as laser rangefinder, video camera, vehicle borne radar, etc.

Subjected to various environmental factors, the data clock information is often out of sync because of different data acquisition frequency, which leads to the difficulty in data fusion. In this study, according to practical requirements, a multi-sensor environmental perception collaborative method was first proposed; then, based on the principle of target priority, large-scale priority, moving target priority and difference priority, a multi-sensor data fusion optimization algorithm based on convolutional neural network was proposed.

The average unload scheduling delay of the algorithm for test data before and after optimization under different network transmission rates. It can be seen that with the improvement of network transmission rate and processing capacity, the unload scheduling delay decreased after optimization and the performance of the test results is the closest to the optimal solution indicating the excellent performance of the optimization algorithm and its adaptivity to different environments.

In this paper, the results showed that the proposed method significantly improved the redundancy and fault tolerance of the system thus ensuring fast and correct decision-making during driving.

This study aims to explore the enabling and suppressing effects of leader affiliative and aggressive humor on employee knowledge sharing form the lens of emotional contagion process, which provides theoretical reference for the applications of different leader humor style, thereby enhancing employee knowledge sharing.

This study collected three waves of data and surveyed 379 employees in China. Regression analysis, bootstrapping and latent moderation structural equation were adopted to test the hypotheses.

Leader affiliative humor has a positive impact on employee knowledge sharing, whereas leader aggressive humor has a negative impact on employee knowledge sharing. Positive emotion plays a mediating role between leader affiliative humor and employee knowledge sharing, and negative emotion plays a mediating role between leader aggressive humor and employee knowledge sharing. Moreover, supervisor–subordinate Guanxi moderates the relationship between leader affiliative humor and positive emotion, and between leader aggressive humor and negative emotion, respectively.

This study not only adds to the knowledge sharing literature calling for the exploration of antecedents and mechanism of employee knowledge sharing, but also contributes to our comprehensive understanding of the suppressing and enabling effects of leader humor style on employee knowledge sharing. Besides, this study also unpacks the dual-path mechanism and boundary condition between leader humor style and employee knowledge sharing and augments the theoretical explanations of emotional contagion theory between leader humor style and employee knowledge sharing.