Forecasting Key Performance Indicators for Smart Connected Vehicles

被引：0

作者：

Skiold, David ^{[1
]}

Arora, Shivani ^{[1
]}

Mihailescu, Radu-Casian ^{[1
,3
]}

Balaghi, Ramtin ^{[2
]}

机构：

[1] Malmo Univ, Malmo, Sweden

[2] Volvo Cars, Gothenburg, Sweden

[3] Internet Things & People Res Ctr IOTAP, Malmo, Sweden

来源：

ADVANCES IN ARTIFICIAL INTELLIGENCE-IBERAMIA 2022 | 2022年 / 13788卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

As connectivity has been introduced to the car industry, automotive companies have in-use cars which are connected to the internet. A key concern in this context represents the difficulty of knowing how the connection quality changes over time and if there are associated issues. In this work we describe the use of CDR data from connected cars supplied by Volvo to build and study forecasting models that predict how relevant KPIs change over time. Our experiments show promising results for this predictive task, which can lead to improving user experience of connectivity in smart vehicles.

引用

页码：414 / 415

页数：2