A Personalized Search Query Generating Method for Safety-Enhanced Vehicle-to-People Networks

Distracted driving due to smartphone use is one of the key reasons for road accidents. However, the 6G super-heterogeneous network systems and highly differentiated application scenarios require highly elastic and endogenous information services involving the use of smart apps, and related informati...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2021-06, Vol.70 (6), p.5296-5307
Main Authors: Yan, Xiaodan, Zhang, Jiwei, Elahi, Haroon, Jiang, Meiyi, Gao, Hui
Format: Article
Language:English
Subjects:
6G mobile communication
Customization
Generators
Information retrieval
Information services
Internet
personalization
Queries
recommendation
Road accidents
Safety
Search engines
search query
Semantics
Smartphones
Traffic accidents
Vehicles
Vehicular networks
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Summary:Distracted driving due to smartphone use is one of the key reasons for road accidents. However, the 6G super-heterogeneous network systems and highly differentiated application scenarios require highly elastic and endogenous information services involving the use of smart apps, and related information retrieval by drivers in modern Vehicle-to-People (V2P) Networks. The tension raised due to the conflicting attention requirements of driving and information retrieval can be resolved by designing information retrieval solutions that demand minimal user interaction. In this paper, we construct a Personalized Search Query Generator (PSQG) to reduce driver-mobile interaction during information retrieval in the 6 G era. This system has a query generator and a query recommendation component that update two sets of relationships dynamically: one is the query and the title, another is search and recommendation. The proposed system learns a user's intent based on historical query records and recommends personalized queries, thus reducing the driver-mobile interaction time. We deploy the system into a real search engine and conduct several online experiments. These experiments are conducted using a custom constructed dataset comprising ten million samples. We use the BLEU-score metric and perform A/B testing. The results demonstrate that our system can assist users in making precise queries efficiently. The proposed system can improve drivers' safety if used in smartphones and other information retrieval systems in vehicles.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2021.3075626