Identifying Big Five Personality Traits through Controller Area Network Bus Data

Abstract

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.

Estimating Driver Personality Traits From On-Road Driving Data

“…Hence, the experiments are conducted in an environment that is closer to actual conditions. Wang et al [27] used real driving data to estimate a driver's personality. In [27], only driving signals from a straight route were used, while in this study, we use driving data from more road types, such as intersections, to capture diverse driving behavior.…”

“…Wang et al [27] used real driving data to estimate a driver's personality. In [27], only driving signals from a straight route were used, while in this study, we use driving data from more road types, such as intersections, to capture diverse driving behavior. This study significantly expands on the study in [28] by adding an estimation of a driver's cognitive function from real driving data.…”

Psychological Characteristics Estimation from On-Road Driving Data

“…Hence, the experiments can be conducted in an environment that is closer to actual conditions. Wang et al [27] used real driving data to predict a driver's personality. In [27], only driving signals from a straight route were used, while this study uses driving data from more road types, such as intersections, to capture diverse driving behavior.…”

Improved Huff Model for Estimating Urban Rail Transit Station Catchment Areas considering Station Choices

“…However, like buffer rings, they can be poor predictors of catchment areas where proximity is not the only consideration for selecting a particular service. Other factors such as user preferences and traffic supply can also affect people's choices [21][22][23][24]. e Huff model shows its unique advantages in this respect.…”