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The purpose of this study is to investigate the impacts of Chinese young consumers’ traits (fashion leadership, quality consciousness, price consciousness, environmental consciousness and social media usage) as the external factors on their behavioral intention toward using clothing subscription rental services.

Quantitative research was conducted, and empirical data were collected from 255 Chinese college students. Structural equation modeling was conducted to test the proposed hypotheses.

The study offered convincing evidence that fashion leadership, price consciousness and social media usage are the three main drivers of Chinese young consumers’ favorable attitudes toward clothing subscription rental services, which together with social norms further lead to their intention to use clothing subscription rental services. Contrary to expectations, the study showed that consumers’ quality consciousness and environmental consciousness have no significant impact on consumer attitudes toward clothing subscription rental services. Moreover, the findings of the study demonstrated the impact of attitude and subjective norms on Chinese young consumers’ intention to use clothing subscription rental services.

This study contributes to the literature by integrating additional factors (fashion leadership, quality consciousness, price consciousness, consumer environmental knowledge, social media usage) into the traditional theory of reasoned action model to investigate how Chinese young consumers’ characteristics impact their attitudes and how their attitudes and subjective norms impact their intention toward using clothing subscription rental services.

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.