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A QoE Test System for Vehicular Voice Cloud Services

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Abstract

A vehicular voice cloud service has unique advantages that can help drivers reduce their operational reliance on vehicular equipment and increase driving safety. Automobile manufacturers, communications equipment merchants and network operators still lack methods and tools to evaluate vehicular voice cloud services with respect to the end user’s experience. Considering the user behavior and user experience, a lightweight vehicular voice cloud evaluation system is designed in this paper. The system is able to send voice information to a voice cloud server according to user habits and capture packets to analyze the key indicators. The system obtains the quality of experience value through the QoE (quality of experience) quantitative model and graphically displays the value on the map interface so that the tester can analyze the service quality of the voice cloud service in the region with respect to the QoE. The study shows that the vehicle voice cloud evaluation system can avoid complex communication and language processing, evaluate the performance of the service with respect to the end users, and provide strong objective evaluation support for automobile manufacturers and communication equipment manufacturers in product production testing processes.

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Acknowledgements

This work is partly supported by the Natural Science Foundation of Jiangsu Province of China(No.BK20161165); the Applied Fundamental Research Foundation of Xuzhou of China (No. KC17072); and the Open Fund of the Jiangsu Province Key Laboratory of Intelligent Industry Control Technology, Xuzhou University of Technology.

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  1. Jiangsu Province Key Laboratory of Intelligent Industry Control Technology, Xuzhou University of Technology, Xuzhou, 221018, China

    Kailiang Zhang, Lei Chen, Yuan An & Ping Cui

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  1. Kailiang Zhang
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  2. Lei Chen
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  3. Yuan An
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  4. Ping Cui
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Correspondence to Lei Chen.

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Zhang, K., Chen, L., An, Y. et al. A QoE Test System for Vehicular Voice Cloud Services. Mobile Netw Appl 26, 700–715 (2021). https://doi.org/10.1007/s11036-019-01415-3

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