Research on Classification Model of Natural Driving Scenario Based on LightGBM

Yan, Bai; Yin, Ping; Zhao, Shuai; Zhai, Yongjie; Chen, Jianhua

doi:10.1109/icct46805.2019.8947318

Cited by 1 publication

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“…Drivers were classified into two groups: moderate and aggressive. Yan et al [28] decided to use an improved decision tree and gradient boosting technique, called LightGBM. It was analyzed data network combined with lane images of the road.…”

Section: A Determination Of Driver's Profilementioning

confidence: 99%

Data Analysis Techniques in Vehicle Communication Networks: Systematic Mapping of Literature

et al. 2020

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Vehicles are becoming more intelligent and connected due to the demand for faster, efficient, and safer transportation. For this transformation, it was necessary to increase the amount of data transferred between electronic modules in the vehicular network since it is vital for an intelligent system's decisionmaking process. Hundreds of messages travel all the time in a vehicle, creating opportunities for analysis and development of new functions to assist the driver's decision. Given this scenario, this article presents the results of research to found out which data analysis techniques in vehicular communication networks and for which purposes they are designed. The research method adopted was the systematic mapping of literature, where 196 articles were found using a search protocol. All papers were classified according to the established inclusion and exclusion criteria, and the main results contained were discussed. To obtain a clear view of the generated information and support the identification of possible gaps in this field, correlation graphs, and a systematic map was developed. It was possible to verify that the identification of the driver's profile was the most studied application, with the use of neural network techniques to correlate the gathered data.

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Section: A Determination Of Driver's Profilementioning

confidence: 99%