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Identifying Big Five Personality Traits through Controller Area Network Bus Data
Yameng Wang1,2; Nan Zhao1; Xiaoqian Liu1; Sinan Karaburun3; Mario Chen4; Tingshao Zhu1
First AuthorYameng Wang
Corresponding AuthorZhu, Tingshao(
Correspondent Email(tingshao zhu)

As adapting vehicles to drivers’ preferences has become an important focus point in the automotive sector, a more convenient, objective, real-time method for identifying drivers’ personality traits is increasingly important. Only recently has increased availability of driving signals obtained via controller area network (CAN) bus provided new perspectives for investigating personality differences. This study proposes a new methodology for identifying drivers’ Big Five personality traits through driving signals, specifically accelerator pedal angle, frontal acceleration, steering wheel angle, lateral acceleration, and speed. Data were collected from 92 participants who were asked to drive a car along a pre-defined 15 km route. Using statistical methods and the discrete Fourier transform, some time-frequency features related to driving were extracted to establish models for identifying participants’ Big Five personality traits. For these five personality trait dimensions, the coefficients of determination of effective predictive models were between 0.19 and 0.74, the root mean squared errors were between 2.47 and 4.23, and the correlations between predicted scores and self-reported questionnaire scores were considered medium to strong (0.56–0.88). The results showed that personality traits can be revealed through driving signals, and time-frequency features extracted from driving signals are effective in characterizing and identifying Big Five personality traits. This approach could be of potential value in the development of in-car integration or driver assistance systems and indicates a possible direction for further research on convenient psychometric methods. 

Source PublicationJournal of Advanced Transportation
Indexed ByEI
Funding ProjectBMW China Research Project[20170321] ; National Natural Science Foundation of China[31700984] ; Youth Innovation Promotion Association CAS
WOS Research AreaEngineering ; Transportation
WOS SubjectEngineering, Civil ; Transportation Science & Technology
WOS IDWOS:000591575200001
Citation statistics
Document Type期刊论文
Affiliation1.CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
2.School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China
3.BMW China Automotive Trading Ltd., Beijing, China
4.BMW China Services Ltd., Beijing, China
First Author AffilicationKey Laboratory of Behavioral Science, CAS
Recommended Citation
GB/T 7714
Yameng Wang,Nan Zhao,Xiaoqian Liu,et al. Identifying Big Five Personality Traits through Controller Area Network Bus Data[J]. Journal of Advanced Transportation,2020,2020:10.
APA Yameng Wang,Nan Zhao,Xiaoqian Liu,Sinan Karaburun,Mario Chen,&Tingshao Zhu.(2020).Identifying Big Five Personality Traits through Controller Area Network Bus Data.Journal of Advanced Transportation,2020,10.
MLA Yameng Wang,et al."Identifying Big Five Personality Traits through Controller Area Network Bus Data".Journal of Advanced Transportation 2020(2020):10.
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